|
|
%A L. Allison
%T Added distributions for use in clustering (mixture modelling), function
models, regression trees, segmentation, and mixed Bayesian networks in
Inductive Programming 1.2.
%R 2008/224
%I Faculty of Info. Tech., Monash University, Australia 3800
%M APR
%D 2008
%K IP 1.2, IP1.2, TR 224, TR224, c2008, c200x, c20xx, zz0508, LAllison,
machine learning, Geometric, Poisson, Gaussian, probability, model, tree,
segment, network, FP, MDL, minimum message length, MML, description,
inductive inference, II, Student's t-Distribution
%X "Inductive programming is a machine learning paradigm combining functional
programming (FP) with the information theoretic criterion, Minimum Message
Length (MML). IP 1.2 now includes the Geometric and Poisson distributions
over non-negative integers, and Student's t-Distribution over continuous
values, as well as the Multinomial and Normal (Gaussian) distributions from
before. All of these can be used with IP's model-transformation operators,
and structure-learning algorithms including clustering (mixture-models),
classification- (decision-) trees and other regressions, and mixed Bayesian
networks, provided only that the types match between each corresponding
component Model, transformation, structured model, and variable -- discrete,
continuous, sequence, multivariate, and so on."
[more] inc. source code,
[Paper.ps].
%A T. I. Dix
%A D. R. Powell
%A L. Allison
%A J. Bernal
%A S. Jaeger
%A L. Stern.
%T Comparative analysis of long DNA sequences by per element
information content using different contexts.
%J BMC Bioinformatics
%V 8(S2):S10
%M MAY
%D 2007
%K jrnl, ejrnl, MolBio, c2007, c200x, c20xx, zz0407, TIDix, LAllison, sequence,
analysis, data, compression, low, mutual, information content, plot, II, MDL,
AI, context, description, profile, signature, pattern discovery, genomic,
minimum message length, MML, Cyanidioschyzon merolae, alga, algae,
Plasmodium falciparum
%X "... The paper presents a methodology for exploring long DNA sequences, of
the order of millions of bases, by means of their information content. ..."
[more]
[doi:10.1186/1471-2105-8-S2-S10]['08]
%A Minh Duc Cao
%A T. I. Dix
%A L. Allison
%A C. Mears
%T A simple statistical algorithm for biological sequence compression.
%J Data Compression Conf.
%W Snowbird, Utah
%I IEEE
%P 43-52
%M MAR
%D 2007
%K conf, MolBio, II, DCC, DCC07, c2007, c200x, c20xx, zz0307, DNA, protein,
minh, minhc, LAllison, TIDix, MML, MDL, compress, expert model, XM, profile,
models, bioinformatics, dnacompress
%X "This paper introduces a novel algorithm for biological sequence compression
that makes use of both statistical properties & repetition within sequences.
A panel of experts is maintained to estimate the probability distribution of
the next symbol in the sequence to be encoded. Expert probabilities are
combined to obtain the final distribution. The resulting information sequence
provides insight for further study of the biological sequence. Each symbol is
then encoded by arithmetic coding. Experiments show that our algorithm
outperforms existing compressors on typical DNA & protein sequence datasets
while maintaining a practical running time."
[more]
[reprint.pdf]
[doi:10.1109/DCC.2007.7]['07]
%A R. T. O'Donnell
%A K. B. Korb
%A L. Allison
%T Causal KL: Evaluating causal discovery.
%I Faculty of Info. Tech., Monash University, Clayton, Australia 3800
%R 2007/207
%P 26
%M FEB
%D 2007
%K TR 207, TR207, c2007, c200x, c20xx, LAllison, rodo, KBKorb, AI, II,
causal model, intervention, interventions, Bayesian network, networks, BN,
models, Causal Kullback Leibler distance, KL, CKL, C-KL, Bnet, Bnets
%X "The two most commonly used criteria for assessing causal model discovery
with artificial data are edit-distance & Kullback-Leibler divergence,
measured from the true model to the learned model. Both of these metrics
maximally reward the true model. However, we argue that they are both
insufficiently discriminating in judging the relative merits of false models.
Edit distance, for example, fails to distinguish between strong & weak
probabilistic dependencies. KL divergence, on the other hand, rewards
equally all statistically equivalent models, regardless of their different
causal claims. We propose an augmented KL divergence, which we call Causal
KL (CKL), which takes into account causal relationships which distinguish
between observationally equivalent models. Results are presented for three
variants of CKL, showing that Causal KL works well in practice."
[pdf]['07]
[abs]['07]
Also see [MML]
%A M. B. Dale
%A L. Allison
%A P. E. R. Dale
%T Segmentation and clustering as complementary sources of information.
%J Acta Oecologica
%I Elsevier
%V 31
%N 2
%P 193-202
%M MAR
%D 2007
%K jrnl, ecology, LAllison, c2007, c200x, c20xx, segment, cluster, MML, MDL,
II, minimum message length, description, classification, mixture model,
modelling, BIC, ecological, vegetation, spatial, series, scale, zz0307
%X "... examines the effects of using a
segmentation method to identify change-points or edges in vegetation. It
identifies coherence (spatial or temporal) in place of unconstrained
clustering. ..."
[more]
[doi:10.1016/j.actao.2006.09.002]['07]
Auths 1 & 3 @ Griffith U., auth 2 @ Monash U.
Also see [MML]
%A R. T. O'Donnell
%A L. Allison
%A K. B. Korb
%T Learning hybrid Bayesian networks by MML.
%J AI 2006: Advances in Artificial Intelligence.
19th ACS Australian Joint Conf. on Artificial Intelligence (AI2006)
%I SpringerVerlag
%S LNCS/LNAI
%V 4304
%P 192-203
%D 2006
%K conf, AI2006, c2006, c200x, c20xx, minimum message, MDL, rodo, description,
LAllison, KBKorb, MML, length, II, CPT, logit, classification, CaMML,
decision tree, trees, Dtree, local structure, Markov Chain Monte Carlo,
MCMC, BDE, network, zz1206, model, models, Bnet, Bnets
%X "We use a Markov Chain Monte Carlo (MCMC) MML algorithm to learn hybrid
Bayesian networks from observational data. Hybrid networks represent local
structure, using conditional probability tables (CPT), logit models,
decision trees or hybrid models, i.e., combinations of the three. We compare
this method with alternative local structure learning algorithms using the
MDL and BDe metrics. Results are presented for both real and artificial data
sets. Hybrid models compare favourably to other local structure learners,
allowing simple representations given limited data combined with richer
representations given massive data". isbn:3540497870; isbn13:978-3540497875.
[more]
[doi:10.1007/11941439_23]['06]
[pdf]['06]
also see [MML]
%A T. I. Dix
%A D. R. Powell
%A L. Allison
%A S. Jaeger
%A J. Bernal
%A L. Stern
%T Exploring long DNA sequences by information content.
%I Probabilistic Modeling and Machine Learning in Structural and
Systems Biology, Workshop Proc.
%W Tuusula, Finland
%P 97-102
%M JUN
%D 2006
%K MolBio, wShop, c2006, c200x, c20xx, modelling, models, sequence, MML, MDL,
LAllison, minimum message length, comparative, chromosome, description, 1D,
TIDix, zz0806, approximate repeats, model, ARM, dotter, dot plot, plots, 2D
%X ... long ... order of millions of bases, ...
Also see Dix et al, BMC Bioinf., 8(S2):S10, May '07,
[doi:10.1186/1471-2105-8-S2-S10]['08]
and [Bioinformatics]
CR classn '98: F.2, I.2.6, G.3, J.3.; isbn:9521032774.
%A L. Allison
%T A programming paradigm for machine learning with a case study of
Bayesian networks.
%J ACSC2006
%P 103-111
%M JAN
%D 2006
%K conf, ACSC, ACSW, LAllison, Inductive Programming, IP, c2006, c200x, c20xx,
MML FP, MMLF P, MMLFP, AI, minimum message length, description, MDL, zz0106,
inference, network, net, nets, mixed, generalized, SARS, search and rescue,
SARbayes, models, model, trees, II, missing data, ACSC29, Bnet, Bnets
%X "... combines functional programming for writing statistical models &
information theory to prevent overfitting. ... Inductive programming is
illustrated by a case study of Bayesian networks. Networks are built from
classification- (decision-) trees. Trees are built from partitioning
functions & models on data-spaces. Trees, & hence networks, are general as a
natural consequence of the method. Discrete & continuous variables, &
missing values are handled by the networks. Finally the Bayesian networks
are applied to a challenging data set on lost persons."
[more]; also see TR153 c2004.
[paper.pdf] isbn:1920682309.
also see [MML]
%A L. Allison
%T Inductive inference 1.1.2: Inductive programming and a case study of
Bayesian networks.
%I Faculty of Info. Tech., Monash University, Clayton, Australia 3800
%R 2005/177
%P 18
%M OCT
%D 2005
%K TR 177, TR177, network, c2005, c200x, c20xx, zz1105, minimum message length,
MML, LAllison, description, machine learning, AI, II, csse, lost person,
FP, MMLFP, MMLF P, missing data, mixed, net, nets, tree, trees, dTree, mdl,
generalized, missing data, TR153, search and rescue, SAR, Bnet, Bnets
%X Builds on the mixed Bayesian nets introduced in TR2004/153.
Also see [Ind. Inf.] pages.
%A L. Allison
%T Models for machine learning and data mining in functional programming.
%J J. Functional Programming
%I CUP
%V 15
%N 1
%P 15-32
%M JAN
%D 2005
%O online at JFP 23/7/2004
%K jrnl, JFP, LAllison, FP, inductive inference, II, AI, c2005, c200x, c20xx,
Haskell, unsupervised, supervised, classification, mixture modelling, stats,
decision tree, dTree, cTree, model trees, minimum message length, MML, type,
zz0105, description, probability, statistical model, MDL, MMLFP, MMLF P,
Bayesian
%X "... Haskell & its type system are used to define & analyse the
nature of some problems & tools in machine learning & data mining. Data
types & type-classes for statistical models are developed that allow models
to be manipulated in a precise, type-safe & flexible way. [SMs] considered
inc. prob. distributions, mixture models, function-models, time-series, &
classification- & function-model-trees. The aim is to improve ways of
designing & programming with models, not only of applying them."
[doi:10.1017/S0956796804005301][23/7/2004]
Also see [II] & [more] and
[MML].
%A L. Allison
%T Finding approximate palindromes in strings quickly and simply.
%I School of Computer Science and Software Engineering, Monash University,
Australia 3800
%R 2004/162
%M DEC
%D 2004
%K TR 162, TR162, palindrome, MolBio, bioinformatics, algorithm, string,
LAllison, c2004, c200x, c20xx, zz1204
%X Described are two algorithms to find long approximate palindromes in
a string, for example a DNA sequence. A simple algorithm requires
O(n)-space and almost always runs in O(k.n)-time where n is the
length of the string and k is the number of ``errors'' allowed in the
palindrome. Its worst-case time-complexity is O(n^2) but this does
not occur with real biological sequences. A more complex algorithm
guarantees O(k.n) worst-case time complexity.
[arxiv...pdf][12/'04]
[more]
%A D. R. Powell
%A L. Allison
%A T. I. Dix
%T Modelling alignment for non-random sequences.
%J 17th ACS Australian Joint Conf. on Artificial Intelligence (AI2004)
%W Cairns
%I SpringerVerlag
%S LNCS/LNAI
%V 3339
%P 203-214
%M DEC
%D 2004
%K conf, AI, MolBio, dynamic programming algorithms, DPA, c2004, c200x, c20xx,
similar, sequence, homology, Malignment, minimum message length, MML, MDL,
PRSS, FASTA, BLAST, Smith Waterman, context, significance test, P values,
Pvalue, DNA, dependent, low information content, score, pattern, repeats,
repetitive, shuffling, masking, scoring, quality, Markov, model, models,
hidden, HMM, PHMM, description, pair, strings, DRPowell, LAllison, TIDix,
bioinformatics
%X "Populations of biased, non-random seqs. may cause standard alignment
algorithms to yield false-positive matches & false-negative misses. A std
significance test based on the shuffling of sequences is a partial solutions
applicable to pop'ns that can be described by simple models. Masking-out
low information content intervals throws information away. ... new & general
method, modelling alignment: Population models are incorporated into the
alignment process, which can (& should) lead to changes in the rank-order of
matches between a query seq. & a collection of seqs., compared to results
from std algorithms. The new method is general & places very few conditions
on the nature of the models that can be used with it. We apply modelling-
alignment to local alignment, global alignment, optimal alignment & the
relatedness problem. Results: As expected, modelling-alignment & the
standard PRSS program from the FASTA package have similar accuracy on
sequence populations that can be described by simple models, e.g. 0-order
Markov models. However, modelling-alignment has higher accuracy on popns that
are mixed or that are described by higher-order models: It gives fewer false
positives & false negatives as show by ROC curves & other results from tests
on real and artificial data". isbn:3540240594.
[more] inc software.
[pp.pdf][11/'04]
[also search for: Allison COMPJ c1999]
%A E. Makalic
%A L. Allison
%A A. Paplinski
%T MML inference of RBF neural networks for regression.
%J Brazilian Symp. on Artificial Neural Networks (SBRN)
%I IEEE & Brazillian Computer Soc.
%W Sao Luis
%P 6
%M SEP
%D 2004
%K conf, ANN, network, c2004, c200x, c20xx, Enes, enesm, MML, zz1104, AI, MDL,
minimum message length, radial basis function, RBFs, functions, description,
LAllison, artificial, AIC
%X Also see [MML]
Conf [sbrn] 29/9-1/10/2004, isbn:8589029042.
%A L. Allison
%T Inductive inference 1.1.
%I School of Computer Science and Software Engineering, Monash University,
Australia 3800
%R 2004/153
%P 16
%M MAY
%D 2004
%K TR 153, TR153, TR148, 148, MML, MDL, MMLFP, MMLF, minimum message length,
functional programming, FP, description, CSSE, zz0504, LAllison, AI, hybrid,
Bayesian networks, network, mixture, classification, cluster, clustering,
sequence, classification decision trees, tree, dTree, mixed, model, II,
inductive inference, graphical, statistical model, machine learning, general,
tree, models, net, nets, TR177, 177, c2004, c200x, c20xx, Bnet, Bnets
%X "... Case studies illustrate the [F] style of programming for [II]. ...
Mixtures of Markov Models, Stateful Time-Series, Bayesian network ..."
[TR153.pdf]
[abs']
Also see TR2003/148, TR2005/177 and ACSC2006.
%A L. Allison
%T Inductive inference 1.
%I School of Computer Science and Software Engineering, Monash University,
Australia 3800
%R 2003/148
%M DEC
%D 2003
%K TR148, TR 148, MML, MDL, II, MMLFP, MMLF, minimum, message, length, zz1203,
functional programming, FP, data, type, class, description, CSSE, LAllison,
statistical models, Bayesian, artificial intelligence, AI, machine learning
%X [II/200309/] Also see TR2004/153, JFP c2005, and TR2005/177.
%A E. Makalic
%A L. Allison
%A D. L. Dowe
%T MML inference of single-layer neural networks.
%J Proc. of the 3rd IASTED Int. Conf. on Artificial Intelligence and
Applications,
%W Benalmadena, Spain
%P 636-642
%M SEP
%D 2003
%O TR 2003/142, CSSE, Monash University, Australia OCT 2003
%K conf, IASTED AIA, ANN, artificial neural network, minimum message length, II,
LAllison, NN, MML, MDL, DLD, Enes, enesm, description, c2003, c200x, c20xx,
Bayesian, Hessian, TR142, TR 142
%X "The architecture selection problem is of great importance when designing
neural networks. A network that is too simple does not learn the problem
sufficiently well. Conversely, a larger than necessary network presumably
indicates overfitting & provides low generalisation performance. This paper
presents a novel architecture selection criterion for single hidden layer
feedforward networks. The optimal network size is determined using a version
of the Minimum Message Length (MML) inference method. Performance is
demonstrated on several problems & compared with a Minimum Description
Length (MDL) based selection criterion." isbn:0889863903; issn:14827913.
[reprint.ps]
[abs']
Also see: [TR 2003/142.ps] MML inference of single-layer neural networks,
and [MML]
%A L. Allison
%T Longest biased interval and longest non-negative sum interval.
%J J. Bioinformatics
%V 19
%N 10
%P 1294-1295
%M JUL
%D 2003
%K MolBio, jrnl, sequence analysis, DNA, RNA, zz0703, c2003, c200x, c20xx,
bias, skew, density, low information content, computer algorithm, program,
protein, maximum, maximal, heaviest, longest, segment, segments, intervals,
positive sum, nonnegative total, AT CG rich, ATrich, CGrich, problem, sum,
CABIOS, biased interval, LAllison, average, score, scores, highest
%X ... an algorithm to find the longest interval having at least a specified
minimum bias in a sequence of characters (bases, amino acids),
e.g. ``at least 0.95 (A+T)-rich''. It is based on an algorithm to find
the longest interval having a non-negative sum in a sequence of positive
and negative numbers. In practice, it runs in linear time; this can be
guaranteed if the bias is rational. ... [more, inc' code]
[can analyse long sequences, e.g. whole chromosomes, very quickly.]
[doi:10.1093/bioinformatics/btg135]['05]
[.pdf@bioinformatics.oxfordjournals.org][7/'03]
[.pdf@bioinformatics.oxfordjournals.org][7/'03]
%A L. J. Fitzgibbon
%A D. L. Dowe
%A L. Allison
%T Bayesian posterior comprehension via message from Monte Carlo.
%J 2nd Hawaii Int. Conf. on Statistics and Related Fields (HICS)
%M JUN
%D 2003
%K conf, HICS, HICS2, HICS2003, statistics, Las Vegas, stochastic, MonteCarlo,
MCMC, posterior probability distribution, LAllison, c2003, c200x, c20xx
%X [www][5/'03]
[reprint.pdf][7/'04]
%A L. Allison
%T The types of models.
%J 2nd Hawaii Int. Conf. on Statistics and Related Fields (HICS)
%M JUN
%D 2003
%K HICS, HICS2, c2003, c200x, c20xx, MML, MMLFP, MMLF P, MML F P, FP, MDL,
conf, stats, type, model, II, AI, LAllison
%X [abs]
[.ps]
Also see [L.A.]
%A L. J. Fitzgibbon
%A L. Allison
%A J. W. Comley
%T Probability model type sufficiency.
%J 4th Int. Conf. on Intelligent Data Engineering and
Automated Learning (IDEAL-2003)
%W Hong Kong
%P 530-534
%M MAR
%D 2003
%O Springer, LNCS 2690
%K conf, IDEAL, IDEAL4, IDEAL2003, statistical model, sufficient statistics,
stats, c2003, c200x, c20xx, LAllison
%X [more]
[more][5/'03]
[reprint.pdf][7/'04]
Hong Kong, 21-23 March 2003, EUR99.0, isbn:354040550X.
%A J. W. Comley
%A L. Allison
%A L. J. Fitzgibbon
%T Flexible decision trees in a general data-mining environment.
%J 4th Int. Conf. on Intelligent Data Engineering and
Automated Learning (IDEAL-2003)
%W Hong Kong
%P 761-767
%M MAR
%D 2003
%O Springer, LNCS 2690
%K conf, IDEAL, IDEAL4, IDEAL2003, decision tree, DTree, c2003, c200x, c20xx,
classification trees, CDMS, LAllison, MML, MDL, AI, II
%X [more]
[more]
[reprint.pdf]
Hong Kong, 21-23 March 2003, EUR99.0, isbn:354040550X.
%A L. Allison
%T Types and classes of machine learning and data mining.
%J 26th Australasian Computer Science Conference (ACSC)
%P 207-215
%M FEB
%D 2003
%O ACS Series Conferences in Research and Practice in
Information Technology V16;
Australian Computer Science Communications, V25, #1, 2003.
%K conf, ACSC, ACSC26, ACSC2003, type, class, check, model, models, regression,
time series, functional programming, FP, MML, MMLF P, MMLFP, Haskell, type,
class, Haskell98, minimum message length, description, semantics, MDL,
formal, inductive inference, stats, statistics, AI, II, zz0203, MDL,
c2003, c200x, c20xx, LAllison
%X [paper]
[.ps]
isbn:0909925941; issn:14451336.
%A L. J. Fitzgibbon
%A D. L. Dowe
%A L. Allison
%T Message from Monte Carlo.
%R 2002/107
%I School of Computer Science and Software Engineering, Monash University,
Australia 3800
%M DEC
%D 2002
%K TR 107, TR107, minimum message length, MML, description, MDL, inference, II,
Markov Chain Monte Carlo, MCMC, MMLD, reversible, jump, approximation,
parameter estimation, AI, Bayesian, Montecarlo, Las Vegas, Lasvegas,
model, modelling, c2002, c200x, c20xx, zz1202, LAllison, univariate,
polynomial regression, importance sampling
%X "... [MML] is used to partition a sample from an importance sampling density
of the model parameters into coding regions. The regions contain models that
are similar & hence the method can be loosely described as model clustering.
... The method is made practical by using the MML Boundary Rule & importance
sampling. The MML Boundary Rule is a result that applies to MML
approximations in a common form & which states that the boundary of the
optimal coding region will be isometric or approximately isometric. This
means that we need only consider regions with isometric boundaries.
Importance sampling is used to efficiently evaluate expectations & integrals
using Monte Carlo integration, ... The method requires a sample from a
suitable importance sampling density & a means of approximating the
Kullback-Leibler distance between any two models. For general use, we
suggest that the posterior is suitable for the importance sampling. In this
case the Message from Monte Carlo methodology becomes aligned with Bayesian
posterior sampling & can be considered as a means of introspection into the
posterior sample.
We give an example of the method applied to univariate polynomial regression.
[It] was chosen because it is a useful & intuitive problem for which the
MML87 method can & already has been applied. We use the posterior as an
importance sampling density & the polynomial parameters are sampled using
Reversible Jump Markov Chain Monte Carlo."
Also see ICML2002, HICS2003 & [MML];
[abs][12/'02]
%A L. Allison
%T Model classes.
%I School of Computer Science and Software Engineering, Monash University,
Australia 3800
%R 2002/125
%M NOV
%D 2002
%K TR 125, TR125, c2002, c200x, c20xx, zz1102, LAllison
%X Also see [ACSC26 '03]
and [II].
%A L. J. Fitzgibbon
%A D. L. Dowe
%A L. Allison
%T Change-point estimation using new minimum message length approximations.
%J Seventh Pacific Rim Int. Conf. on Artificial Intel. (PRICAI-2002)
%W Tokyo
%I SpringerVerlag
%S LNAI
%V 2417
%P 244-254
%M AUG
%D 2002
%S LNAI
%K conf, PRICAI, PRICAI7, PRICAI02, PRICAI2002, AI, fairly strict, FSMML, MMLA,
minimum message length, MML, inductive inference, II, description, MDL,
segmentation, MMLD, LNAI, LAllison, DLDowe, Monash, csse, zz0902, c2002,
c200x, c20xx
%X Authors at Comp. Sci. and S.W.E., Monash Uni., .au
[abs']; isbn:3540440380.
[reprint.html]
[@springer][10/'02]
%A L. J. Fitzgibbon
%A D. L. Dowe
%A L. Allison
%T Univariate polynomial inference by Monte Carlo message length approximation.
%J Int. Conf. Machine Learning 2002
%W Sydney
%I Morgan_Kaufmann
%P 147-154
%M JUL
%D 2002
%K conf, ICML, ICML2002, ICML02, AI, inductive inference, II, LAllison, DLDowe,
Minimum Message Length, MML, description, MDL, Las Vegas, MonteCarlo, MMC,
Markov chain, MCMC, polynomial, fit, fitting, regression, Gibbs sampling,
FSMML, SRM, univariate polynomial, regression, zz0702, c2002, c200x, c20xx
%X Message from Monte Carlo (MMC) algorithm ... compared with
MML87 and structural risk minimization (SRM).
Authors: Comp. Sci. and S.W.E., Monash Uni., .au. Also see TR 2002/107.
[abs'], issn:10491910.
[reprint.html]
[L.A. on MML]
%A L. Stern
%A L. Allison
%A R. L. Coppel
%A T. I. Dix
%T Discovering patterns in Plasmodium falciparum genomic DNA.
%J Molecular and Biochemical Parasitology
%V 118
%N 2
%P 175-186
%M DEC
%D 2001
%K MolBio, LAllison, jrnl, c2001, c200x, c20xx, Plasmodium falciparum,
minimum message length, description, Markov, MDL, MBP, MML, Malaria, entropy,
sequence analysis, HMM, PFSA, PFSM, information content, genome, DNA,
pattern, repeat, repeats, DNA models, approximate repeats model, repeat,
pattern discovery, knowledge, hidden, GHMM, Bayesian, LStern, TIDix,
bioinformatics
%X [paper.pdf]
[paper]
[doi:10.1016/S0166-6851(01)00388-7][6/'04]
More on [compression] and
[Bioinformatics]
%A L. J. Fitzgibbon
%A L. Allison
%A D. L. Dowe
%T Minimum message length grouping of ordered data.
%J Proc. 11th Int. Workshop on Algorithmic Learning Theory (ALT'2000)
%E H. Arimura
%E S. Jain
%E A. Sharma
%P 56-70
%W Sydney
%S LNCS
%V 1968
%M DEC
%D 2000
%K conf, minimum message length, MML, segment, minimum description length, MDL,
ALT9, ALT2000, cut point, points, segmenting, time series, sequence analysis,
complexity, measurement accuracy, model, LAllison, DLDowe, BIC, Monash, II,
segmentation, problem, data, level, levels, c2000, c200x, c20xx
%X Given a series of multivariate data, approximate it by a piece-wise
constant function. How many cut-points are there? What are the means
and variances of each segment? Where should the cut points be placed?
The simplest model is a single segment. The most complex model has
one segment per data point. The best model is generally somewhere
between these extremes. Only by considering model complexity can
a reasonable inference be made.
[reprint.ps]
[summary]; isbn:3540412379.
%A D. R. Powell
%A L. Allison
%A T. I. Dix
%T Fast, optimal alignment of three sequences using linear gap costs.
%J J. Theor. Biol.
%V 207
%N 3
%P 325-336
%M DEC
%D 2000
%K jrnl, JTB, MolBio, Biology, multiple, sequence, alignments, similarity,
affine, linear, cost, gaps, insert, delete, indel, indels, DNA, time, speed,
fast, string, strings, iterative, phylogenetic, family tree, Ukkonen,
edit distance, dynamic programming algorithm, DPA, DRPowell, LAllison, TIDix,
c2000, c200x, c20xx, zz1100, J Theoretical Biology, bioinformatics
%X [...] The obvious dynamic programming algorithm for optimally
aligning k sequences of length n runs in O(n^k) time. This is
impractical if k >= 3 and n is of any reasonable length.
[...] new algorithm [is] guaranteed to find the optimal alignment [...]
particularly fast when the (three-way) edit distance is small. [...]
O(n + d^3) on average.
[paper][11/'00] and code,
[doi:10.1006/jtbi.2000.2177]['04]
more on [bioinformatics]
%A L. Allison
%A D. Powell
%A T. I. Dix
%T Modelling is more versatile than shuffling
%R 2000/83
%I School of Computer Science and Software Engineering, Monash University,
Australia 3168
%D 2000
%K MolBio, pair, two, sequence, alignment, PFSA, hidden Markov model, PHMM,
low medium information content, repeat, repetition, structure, pattern,
model, shuffle, shuffling, randomize, DNA, permute, tuples, frequencies,
family, PFSM, HMM, dynamic programming algorithm, DPA, homology, algorithm,
minimum message length, MML, description, MDL, LAllison, DRPowell, TIDix,
mAlignment, TR 83, TR83, c2000, c200x, c20xx, bioinformatics
%X It is shown how to incorporate almost any (left to right) model of a
population of sequences into the alignment DPA. Doing so is an alternative
to shuffling/ randomizing (Fitsch; Altschul, Erickson ...) the sequences
to correct for population biases. The resulting algorithm gives
fewer false positives, fewer false negatives, and can (and should)
change the rank ordering of alignments.
[also search for: modelling alignment]
[www],
[Bioinformatics]
%A L. Allison
%A L. Stern
%A T. Edgoose
%A T. I. Dix
%T Sequence complexity for biological sequence analysis.
%J Computers and Chemistry
%V 24
%N 1
%P 43-55
%D 2000
%K jrnl, comp chem, MolBio, data compression, compress, MML, MDL, DNA, mine,
approximate repeats, repeat, repetitive, complexity, high, moderate, low,
information content, bioinformatics, LAllison, TIDix, c2000, c200x, c20xx,
computational biology and chemistry, LStern, minimum message length,
bioinformatics, AI, inductive inference, data mining, time series,
timeseries, description
%X J is now "Computational Biol. & Chem." at c2006.
[paper.pdf]
[paper]
[doi:10.1016/S0097-8485(00)80006-6]['02]
[[NB. Journal later known as COMPUTATIONAL BIOLOGY AND CHEMISTRY, '06]]
also see [Bioinformatics]
%A L. Allison
%T Generator and search objects in Java.
%J J. Res. and Practice in Inf. Tech.
%V 32
%P 3-12
%N 1
%D 2000
%K jrnl, ACS, JRPIT, continuation, c2000, c200x, c20xx, LAllison, object, OOP,
generators
%X [.pdf],
more on [java],
also see TR97/317
%A T. Edgoose
%A L. Allison
%T MML Markov classification of sequential data.
%J Stats. and Comp.
%I Kluwer Academic
%V 9
%N 4
%P 269-278
%M SEP
%D 1999
%K jrnl, stats, computing, minimum message length, MML, description, MDL,
statistical, inductive inference, clustering, classification, II, AI,
sequence, data analysis, model, algorithm, mining, series, forwards,
backwards, LAllison, c1999, c199x, c19xx, Snob, time series, timeseries
%X [paper]
[more]
%A D. R. Powell
%A L. Allison
%A T. I. Dix
%T A versatile divide and conquer technique for optimal string alignment.
%J IPL
%V 70
%N 3
%P 127-139
%D 1999
%K IPL, jrnl, dynamic programming algorithm, DPA, Hirschberg, Ukkonen, Myers,
MolBio, strings, Edit Distance, similarity, sequence analysis, LCS, Fast,
Time, Speed, Linear Space, Check Point, pointing, checkpoint, algorithms,
DRPowell, LAllison, TIDix, bioinformatics, c1999, c199x, c19xx, zz0899
%X A check-pointing (CP) technique uses O(n) space but is simpler
than Hirschberg's O(n)-space technique; H' ('97) attributes
an O(N**2)-time simple edit-distance CP to Eppstein. Here, CP
is applied to more complex cost functions, e.g. linear gap costs, and ...
to Ukkonen's O(n*d)-time DPA, even including linear gap costs,
to give O(n)-space, O(n.log d + d**2)-average-time,
effectively O(d**2)-time in many practical situations.
[doi:10.1016/S0020-0190(99)00053-8][6/'04]
[more]
%A L. Allison
%A D. Powell
%A T. I. Dix
%T Compression and approximate matching.
%J COMPJ
%V 42
%N 1
%P 1-10
%D 1999
%K jrnl, MolBio, COMPJ, Computer Journal, LAllison, DRPowell, TIDix, pair,
string, strings, sequence, alignment, analysis, algorithm, homology,
similarity, limits, HMM, MML, MDL, II, normalized, limit, significance test,
testing, jie, med, DPA, low information content, repeats, repetitive, wei,
non-random, nonrandom, compressible, bioinformatics, pair, probabilistic,
information theory, features, complexity, time, fast, speed, shuffling,
shuffle, randomize, DNA, edit distance, hidden Markov model, HMM, PHMM,
c19xx, c1999, c199x, modelling, Malignment, context dependent, scoring
%X A population of sequences is called non-random if there is a statistical
model and an associated compression algorithm that allows members of the
population to be compressed, on average. Any available statistical model
of a population should be incorporated into algorithms for alignment of
the sequences and doing so changes the rank-order of possible alignments
in general. The model should also be used in deciding if a resulting
approximate match between two sequences is significant or not. It is
shown how to do this for two plausible interpretations involving pairs
of sequences that might or might not be related. Efficient alignment
algorithms are described for quite general statistical models of sequences.
The new alignment algorithms are more sensitive to what might be termed
`features' of the sequences. A natural significance test is shown to be
rarely fooled by apparent similarities between two sequences that are merely
typical of all or most members of the population, even unrelated members.
[more]
[doi:10.1093/comjnl/42.1.1]['06]
[pdf@compj]['05]
[.pdf@compj]['04]
[Also search for: Powell AI2004], and see [bioinformatics].
%A L. Allison
%A T. Edgoose
%A T. I. Dix
%T Compression of strings with approximate repeats.
%J Intell. Sys. in Mol. Biol. '98
%P 8-16
%M JUN
%D 1998
%K conf, MolBio, c1998, c199x, c19xx, LAllison, Monash, ISMB6, ISMB98, ISMB 98,
DPA, inductive inference, II, DNA, Chen, Behzadi, F. Le Fessant, protein,
sequence analysis, data, text, repeat, compression, bioinformatics,
repetitive, Kwong, hidden Markov model, HMM, low information content, Li,
lines, ALU, sines, ming, self similarity, models, minimum message length,
MML, description, MDL
%X [paper] ISMB98, 28 June - 1 July 1998,
[.pdf] Montreal, Canada; isbn:1577350537.
[bioinformatics]
%A L. Allison
%T Information-theoretic sequence alignment.
%I School of Computer Science and Software Engineering, Monash University
%R 98/14
%M JUN
%D 1998
%K TR14, TR 14, MolBio, LAllison, string, strings, similarity, edit-distance,
homology, approximate match, matching, DNA, DPA, hidden Markov model, HMM,
low information content, repeats, repetitive, compressible, MML, MDL,
data compression, content, sequences, c1998, c199x, c19xx, bioinformatics
%X [TR98/14 (HTML)]
Also see: Allison, Powell and Dix, `Compression and Approximate Matching',
Comp. J. 42(1) pp1-10, 1999, for a fuller explanation and later results.
[Bioinformatics]
%A G. Pringle
%A L. Allison
%A D. L. Dowe
%T What is a tall poppy among web pages?
%J Proc. 7th Int. World Wide Web Conference (in Comp. Networks and ISDN Sys.)
%V 30
%N 1-7
%P 369-377
%M APR
%D 1998
%O Comp. Networks and ISDN Systems (Jrnl)
%I Elsevier
%K conf, WWW, WWW7, WWW98, WWWC, WWWC7, WWWC98, IWWWC, IWWWC7, IWWWC98,
decision tree, DT, search engine, engines, rating, ranking, Lycos, Infoseek,
Altavista, Alta vista, page, pages, LAllison, DLDowe, GPringle, Monash,
c1998, c199x, c19xx, zz0598
%X [more]
Uses inductive inference technology to infer why some web
pages rank highly on certain Internet Search Engines.
Conference held 14-18 April 1998, Brisbane, Australia. isbn:0169755298.
%A D. L. Dowe
%A L. Allison
%A G. Pringle
%T The hunter and the hunted - modelling the relationship between web pages
and search engines.
%J 2nd Pacific-Asia Conf. on Knowledge Discovery and Data Mining (PAKDD98)
%P 380-382
%M APR
%D 1998
%I SpringerVerlag
%S LNAI
%V 1394
%K conf, PAKDD2, PAKDD 98, internet, web page, decision tree, engine,
Dtree, LAllison, GPringle, DLDowe, Monash, c1998, c199x, c19xx
%X isbn:3540643834. Also see Pringle et al in WWW7 pp.369-377 c1998.
%A T. Edgoose
%A L. Allison
%T Minimum message length hidden Markov modelling.
%J Data Compression Conf.
%W Snowbird, Utah
%I IEEE
%P 169-178
%M MAR
%D 1998
%K conf, DCC, DCC98, model, MML, MDL, HMM, inductive inference, II, Rissanen,
sequence, model, series, clustering, classification, data analysis, CSci,
description, complexity, time series, timeseries, CompSci, LAllison, Monash,
Snob, c1998, c199x, c19xx, Snowbird, AI
%X [more]
[pdf @ DCC][7/'04]
[pdf @ DCC]['98] - may require IEEE a/c
%A T. Edgoose
%A L. Allison
%T Unsupervised Markov classification of sequence data using MML.
%J Proc. 21st Australian Comp. Sci. Conf.
%E C. McDonald
%W Perth
%I SpringerVerlag
%P 81-94
%M FEB
%D 1998
%K ACSC, ACSC21, ACSC98, inductive inference, II, time series, model, HMM,
serial correlation, SNOB, AI, machine learning, hidden, data mining, MDL,
LAllison, Monash, class, cluster, clustering, time series, timeseries,
c1998, c199x, c19xx, zz0198
%X also see [MML]
isbn:9813083905; order from...[springer]
%A D. R. Powell
%A L. Allison
%A T. I. Dix
%A D. L. Dowe
%T Alignment of low information sequences.
%J Australian Computer Science Theory Symposium, CATS '98
%W Perth
%P 215-230
%I NUS
%M FEB
%D 1998
%K conf, MolBio, align, HMM, DPA, DNA, ACSC, CATS, CATS98, LAllison, DLDowe,
bioinformatics, probability, features, TIDix, Monash, c1998, c199x, c19xx,
DRPowell
%X [paper.ps]['98]
[~powell][5/01]; isbn:9813083921.
%A T. Edgoose
%A L. Allison
%A D. L. Dowe
%T An MML classification of protein structure that knows about angles and
sequence.
%J Pacific Symposium on Biocomputing '98
%P 585-596
%M JAN
%D 1998
%K conf, MolBio, PSB, PSB3, PSB98, von Mises, vonMises, angle, dihedral, class,
cluster, clustering, HMM, SNOB, time series, timeseries, ARC A49602504,
LAllison, MDL, distribution, bioinformatics, Monash, c1998, c199x, c19xx
%I World Scientic
%X SNOB + vonMises circular probability distribution + 1st order Markov model.
phi-psi pairs give 17 classes and a class seq' correlation matrix.
[paper]
[paper]
[paper.pdf@stanford.edu]['98]; isbn:9810232780.
von Mises, probability density:
f(x | mu, kappa) = (1/(2.pi.I0(kappa))).exp(kappa.cos(x-mu))
where I0(kappa) is a normalisation constant.
[Bioinformatics]
%A D. R. Powell
%A D. L. Dowe
%A L. Allison
%A T. I. Dix
%T Discovering simple DNA sequences by compression.
%J Pacific Symposium on Biocomputing '98
%P 597-608
%M JAN
%D 1998
%K conf, MolBio, PSB, PSB3, PSB98, repeat, repetition, data, model, random,
ARC A49602504, bioinformatics, DRPowell, LAllison, DLDowe, TIDix, Monash,
zz0198, c1998, c199x, c19xx
%I World Scientic
%X [paper.pdf]['98]
[paper]
isbn:9810232780.
Also see [MML page]
and Milosavljevic '95.
%A K. B. Korb
%A C. Kopp
%A L. Allison
%T Higher education policy in Australia.
%J Australian Rationalist
%N 45
%P 16-26
%M DEC
%D 1997
%O `A Statement on Higher Education Policy in Australia',
TR 97/318, Department of Computer Science, Monash University, 1997
%K jrnl, TR318, TR 318, HER, West Review, University, Universities, funding,
Government policy, trends, technology, quality, Carlo, LAllison, zz1297,
c1997, c199x, c19xx
%X [.ps.gz]
[html]
%A L. Allison
%T Java continues Prolog-like
%I Department of Computer Science, Monash University
%R 97/317
%M APR
%D 1997
%K TR317, TR 317, LAllison, c1997, c199x, c19xx
%X see: Allison, JRPIT, c2000.
%A T. C. Edgoose
%A L. Allison
%T Mixture modelling of sequential datasets.
%R 96/285
%I Department of Computer Science, Monash University
%M NOV
%D 1996
%K TR285, TR 285, sequence, series, HMM, hidden Markov model, classification,
clustering, Snob, minimum message length, MML, description, MDL, c1996,
inductive inference, II, data sets, analysis, LAllison, Monash, c199x, c19xx
%X Data is a sequence of items. Each item is a number of measurements.
The program clusters items into classes. Each class is described
by distributions (currently finite state, or Normal, or von-Mises)
on attributes. The program determines the number and form of each class.
The class of an item can be influenced by those of its neighbours.
Partial assignment allows for uncertainty of class membership.
Tested on artificial and real data including some weather data
and dihedral angles from protein structures.
Also see: Snowbird DCC98
and [MML]
%A D. L. Dowe
%A L. Allison
%A T. I. Dix
%A L. Hunter
%A C. S. Wallace
%A T. Edgoose
%T Circular clustering of protein dihedral angles by minimum message length.
%J Pacific Symposium on Biocomputing '96
%M JAN
%P 242-255
%D 1996
%I World Scientific
%O TR 95/237, Dept. Computer Science, Monash University, Oct 1995
%K PSB, PSB96, TR 237, TR237, Monash, DLD, CSW, CSWallace, LAllison, MolBio,
Monash, classification, angle, von Mises, vonMises, protein structure,
inductive inference, II, MML, MDL, conf, bioinformatics, c1996, c199x, c19xx
%X L. Hunter - NLM, NIH. PSB '96: 3-6 Jan' 1996, Hawaii, isbn:9810225784.
[Bioinformatics]
[paper.ps][1/'96]
[[eProceedings]][1/'96]
%A R. A. Baxter
%A L. Allison
%A K. Korb
%T Regulation of on-line information services (a submission).
%M AUG
%D 1995
%K censor, censorship, regulation, control, internet, inter net, Monash,
world wide web, LAllison, www, HREF, http, c1995, c199x, c19xx
%X Submission to the Dept. of Communications and the Arts re their
consultation paper on the regulation of online information services
of 7 July 1995.
Also to the Senate Enquiry into Regulation of Computer On-Line Services
(closing date for submissions 29 Sept 1995)
See also TR224.
%A L. Allison
%T Towards modelling evolution = mutation modulo selection in sequence
alignment.
%R 95/225
%I Dept. Computer Science, Monash University.
%M JUN
%D 1995
%K LAllison, Monash, TR225, TR 225, MolBio, evolution, pressure, selection,
fit, fitness, family, phylogenetic, evolutionary tree, trees, sequence,
multiple alignment, zz0795, c1995, c199x, c19xx, bioinformatics
%X [bioinformatics]
%A L. Allison
%A R. A. Baxter
%T Protecting Our Innocents.
%R 95/224
%I Dept. Computer Science, Monash University.
%M JUN
%D 1995
%K LAllison, Monash, TR224, TR 224, internet, inter net, world wide web,
child, children, kids, K12, porn, pornography, violence, politics, sex,
online, censorship, censor, ethics, ethical, CICA, PICS, surfwatch,
www, HREF, http, zz0695, topical, c1995, c199x, c19xx
%X Argues that internet authors (www, ftp, ...) should be encouraged,
NOT required, to describe their works using annotations that are
objective and machine readable. (This is not a censorship proposal and
it cannot be (mis)used as one.)
[TR 95/224 here]
Also see: Regulation of on-line information services (a submission), Aug 1995
%A L. Allison
%T Toward a Denotational Semantics for
<A HREF="http://www.cs.monash.edu.au/~lloyd/tildeFP/LambdaLog">LFP</A>
= Logic + Functional Programming, Coded in Lazy ML.
%R 94/204
%M SEP
%D 1994
%I Dept. Computer Science, Monash University
%K LAllison, logic, functional, programming, LP, FP, LFP, FLP, LambdaLog,
LML, Monash, TR 204, TR204, c1994, c199x, c19xx
%X [LFP]
[FP]
[FP]
%A L. Allison
%T Using Hirschberg's algorithm to generate random alignments of strings.
%J IPL
%V 51
%N 5
%P 251-255
%M SEP
%D 1994
%K LAllison, Monash, jrnl, IPL, MolBio, DNA, bioinformatics,
MML, minimum message length encoding, II, inductive inference, Hirschberg,
string, sequence, alignment, similarity, homology, approximate, match,
matching, LCS, LCSS, edit distance, Gibbs sampling, GS, random, sample, DPA,
simulated annealing, SA, dynamic programming algorithm, divide and conquer,
hidden Markov model, HMM, probability, posterior distribution, stochastic,
c1994, c199x, c19xx
%X Hirschberg's (CACM '75) recursive divide and conquer technique for
the dynamic programming technique (LCS, LCSS, Edit Distance) is
applied to the problem of sampling alignments of two strings
at RANDOM from the alignments' posterior probability distribution.
[reprint.ps]
[doi:10.1016/0020-0190(94)90004-3]['04]
[Bioinformatics]
%A L. Allison
%A C. S. Wallace
%T An information measure for the string to string correction problem with
applications.
%J 17th Australian Comp. Sci. Conf.
%P 659-668
%M JAN
%D 1994
%W Christchurch, N. Z.
%K LAllison, CSW, CSWallace, Monash, conf, MolBio, inductive inference, II,
string, sequence, family, evolutionary, phylogenetic, tree, trees,
variation, variance, uncertainty, estimate, estimation, parameters, DNA,
multiple alignment, Gibbs sampling, sample, GS, simulated annealing SA,
minimum message length MML, Bayesian, temperature, cooling, probabilistic,
NZ, New Zealand, c1994, c199x, c19xx, ACSC 17, 94, ACSC17, ACSC94,
bioinformatics, Monash
%O Australian Comp. Sci. Comm., Vol 16, No 1(C), 1994, isbn:047302313X.
%X It has been shown how to calculate a probability for an alignment.
Alignments are sampled from their posterior probability distribution.
This is extended to multiple alignments (of several strings). Averaging
over many such alignments gives good estimates of how closely the strings
are related and in what way. In addition, sampling in an increasingly
selective way gives a simulated annealing search for an optimal alignment.
[paper]
[Bioinformatics]
See also the related paper J. Mol. Evol. (39, pp418-430, 1994)
The posterior probability distribution ... for more results.
%A B. M. Jenkins
%A A. J. Davison
%A L. Allison
%A A. J. Maeder
%T A perimeter based technique for fusion of multi-sourced micrographic images.
%J Dicta-93
%W Sydney Australia
%M DEC 8-10
%D 1993
%P 556-563
%K LAllison, Monash, conf, image processing IP, shape,
signature, DICTA DICTA93, Monash, c1993, c199x, c19xx
%X [MML]
%A L. Allison
%A C. S. Wallace
%T The posterior probability distribution of alignments and its application
to parameter estimation of evolutionary trees and to optimization of
multiple alignments.
%J J. Mol. Evol.
%V 39
%N 4
%P 418-430
%M OCT
%D 1994
%O an early version: TR 93/188, Dept. Computer Science, Monash U., July '93
%K MolBio, jrnl, JME, LAllison, CSW, bioinformatics, optimisation, estimating,
inferring, parameters, multiple, alignment, DNA, data, string, molecular,
sequence, homology, LCS, family, phylogenetic, tree, trees, edit distance,
dynamic programming algorithm, DPA, stochastic, Monte Carlo method,
CSWallace, methods, GS, Gibbs sampling, sample, MonteCarlo, speed, Bayesian,
c1994, c199x, c19xx, simulated annealing, SA, inductive inference, II,
minimum message length encoding, MML, minimum description length, MDL,
transthyretin, TR188, chloramphenicol resistance gene, CAT, CATB, CATD,
CATP, CATQ, CCOLI, ECOLI, algorithmic, mutual information, theory,
significance, probabilistic, temperature, bioinformtics, limits, TR 93/188
%X It is shown how to sample alignments from their posterior probability
distribution given two strings. This is extended to sampling alignments of
more than two strings. The result is firstly applied to the estimation of
the edges of a given evolutionary tree over several strings. Secondly,
when used in conjunction with simulated annealing, it gives a stochastic
search method for an optimal multiple alignment.
[paper] and source code,
[reprint.ps]
[doi:/10.1007/BF00160274]['07]
(The JME paper is a much expanded and changed version of TR 93/188.)
[TR93/188](.ps)
%A A. Finlay
%A L. Allison
%T A correction to the denotational semantics for Prolog of Nicholson and Foo.
%I ACM
%J TOPLAS
%V 15
%N 1
%P 206-208
%M JAN
%D 1993
%K TOPLAS, jrnl, note, LAllison, Alan, alanf, Monash, DS, semantic, Prolog,
logic programming, LP, c1993, c199x, c19xx, Monash
%X [doi:10.1145/151646.151652][2/'04]
Also see [Logic Programming]
and [Denotational Semantics]
%A L. Allison
%T Applications of recursively defined data structures.
%J Aust. Comp. J.
%V 25
%N 1
%P 14-20
%M FEB
%D 1993
%K jrnl, LAllison, Monash, ACJ, functional applicative programming, FP,
lazy, need, circular, program, recursive, recursion, data structure,
list, lists, search tree, trees, breadth first traversal, queue, memo,
Fib, Fibonacci, n-queens, nqueens, n queens, irreducible, good,
sequences, Axel Thue, c1993, c199x, c19xx, TR 88 119 TR88-119 TR119
%O an early version in TR 88/119 Dept. Computer Science, Monash University '88
%X A circular program contains a data structure whose definition is
self-referential or recursive. The use of such a definition allows
efficient functional programs to be written and can avoid the creation of
intermediate data structures that would have to be garbage collected.
This paper uses circular programs in various ways, to implement
memo-structures and explicit search-trees to hold solutions to
constraint-satisfaction problems.
Examples: search trees, n-queens, irreducible sequences.
[reprint.ps]
[html]
%A L. Allison
%T A fast algorithm for the optimal alignment of three strings.
%J J. Theor. Biol.
%V 164
%N 2
%P 261-269
%M SEP
%D 1993
%O TR 92/168 Dept. Computer Science, Monash University, Oct '92.
%K LAllison, Monash, jrnl, II, JTB, MolBio, bioinformatics, multiple alignment,
edit distance, Ukkonen, three, string, strings, sequence, sequences,
dynamic programming algorithm, DPA, TR 92 168 TR92-168 TR168,
c1993, c199x, c19xx, J Theoretical Biology
%X Given 3 strings, length ~ n, 3-way edit-distance d,
O(n.d^2) time algorithm worst case, O(n+d^3) typically.
Tree costs 0/1/2. ie. xxx :0; xxy, xx-, x-- :1; xyz, xy- :2
NB. Each internal node of an unrooted binary tree has 3 neighbours.
[reprint.ps],
[paper] inc' pdf paper and code,
[doi:10.1006/jtbi.1993.1153]['04],
more on [bioinformatics]
%A L. Allison
%T Normalisation of affine gap costs used in optimal sequence alignment.
%J J. Theor. Biol.
%M MAR
%D 1993
%V 161
%N 2
%P 263-269
%K LAllison, Monash, jrnl, MolBio, JTB, alignment, string, sequence analysis,
edit distance, homology, gap, gaps, linear, indel, insert delete, Altschul,
mutual information, similarity, hidden Markov model, HMM, bioinformatics,
inductive inference, II, c1993, c199x, c19xx, J Theoretical Biology
%X [reprint.ps]
[paper] inc' pdf
also see: Bioinformatics
%A L. Allison
%T Lazy dynamic programming can be eager.
%J IPL
%V 43
%N 4
%P 207-212
%M SEP
%D 1992
%K LAllison, Monash, jrnl, FP, lazy functional programming, fast, efficient,
dynamic programming algorithm, DPA, edit distance, LCS, LCSS, MolBio,
approximate, similar, string strings, match, matching, sequence,
alignment, c1992, c199x, c19xx, bioinformatics
%X Lazy evaluation in a functional language is exploited to make the simple
dynamic programming algorithm for the edit-distance problem run quickly
on similar strings: being lazy can be fast.
Runs in O(n*d) time thanks to laziness.
[reprint.ps]
[html]
[doi:10.1016/0020-0190(92)90202-7]['07]
[Functional Programming]
%A C. N. Yee
%A L. Allison
%T Fast string alignment with linear indel costs.
%R TR 92/165
%I Computer Science, Monash University
%M JUL
%D 1992
%K LAllison, MolBio, string alignment, similarity, homology, Ukkonen,
edit distance, linear indel gap cost, costs, Ukkonen, Monash,
TR 165, TR92/165, TR165, II, c1992, c199x, c19xx, bioinformatics
%X two strings, O(n*d) time.
The constants in the cost function have to be "small" integers.
[Computing for Molecular Biology]
[Also search for: Ukkonen]
%A D. L. Dowe
%A J. Oliver
%A L. Allison
%A T. I. Dix
%A C. S. Wallace
%T Learning rules for protein secondary structure prediction.
%J Proc. 1992 Department Research Conference
%I Dept. Computer Science, University of Western Australia
%E C. McDonald
%E J. Rohl
%E R. Owens
%M JUL
%D 1992
%O TR 92/163, Dept. Computer Science, Monash University, JUN '92
%K LAllison, CSW, DLD, Monash, UWA, WA, conf, MolBio, decision tree, trees,
graph, protein, amino acid, AA, secondary structure, SS, prediction,
rule, rules, alpha helix, beta strand, extended sheet, coil, turn,
CSWallace, inductive inference, II, MML, minimum message length, c1992,
c199x, c19xx, bioinformatics, TR 92 163, TR92-163, TR163
%X [TR92/163.ps]
Also see [Bioinformatics],
and TR 92/163.
{CSci UWA home}; isbn:0864221959.
%A D. L. Dowe
%A J. Oliver
%A T. I. Dix
%A L. Allison
%A C. S. Wallace
%T A decision graph explanation of protein secondary structure prediction.
%J 26th Hawaii Int. Conf. Sys. Sci.
%V 1
%P 669-678
%M JAN
%D 1993
%K LAllison, CSW, Monash, conf, MolBio, protein secondary structure prediction,
conformation, alpha helix, ss, AA, beta sheet extended strand, turn, coil,
II, inductive inference, decision graph tree, DTree, DGraph, CSWallace, CSW,
MML, Minimum message length encoding, description, MDL, Bayesian,
TR163 163, c1993, c199x, c19xx, bioinformatics, HICSS, HICSS26, HICSS93
%X Oliver and Wallace (IJCAI '91) introduced `decision graphs' -
a generalisation of decision trees - here applied to protein secondary
structure prediction.
[paper (HTML)]
[Bioinformatics]
Also see TR 92/163.
%A C. N. Yee
%A L. Allison
%T Reconstruction of strings past.
%J Comp. Appl. BioSci.
%V 9
%N 1
%P 1-7
%M FEB
%D 1993
%O TR 92/162, Dept. Computer Science, Monash University, May '92
%K LAllison, Monash, jrnl, MML, minimum message length, J. Bioinformatics,
encoding, hidden Markov model, HMM, II, inductive inference,
MolBio, string, sequence, alignment, homology, similarity, edit,
evolutionary, distance, parameter estimation, r-theory,
TR TR92-162 TR162 162, c1993, c199x, c19xx, CABIOS, J. Bioinformatics
%X Use of single optimal alignment gives biased estimates of the evolutionary
"distance" between two strings but the r-theory, average all alignments,
recovers accurate estimates over a very wide range of similarity.
[reprint.ps]
[paper.html]
[Bioinformatics]
[now J. Bioinformatics]
%A L. Allison
%T Some algorithmic attacks on multiple alignment (abstract).
%J Boden Conf.
%W Thredbo, Australia
%M FEB
%D 1993
%K LAllison, Monash, RSBS, ANU, conf, MolBio, MML, II, string,
sequence, approximate match, matching, three, DPA, bioinformatics
%X also see [Bioinformatics]
%A L. Allison
%T Estimating parameters and evolutionary distances in the inference of
evolutionary trees (abstract).
%J Robertson Symposium.
%W Australian National University
%M JAN
%D 1993
%K LAllison, Monash, RSBS, ANU, conf, MolBio, MML, inductive inference,
II, string, sequence, multiple alignment, approximate match,
matching, c1993, c199x, c19xx, bioinformatics
%X also see [Bioinformatics]
%A L. Allison
%A C. S. Wallace
%A C. N. Yee
%T Minimum message length encoding, evolutionary trees and multiple alignment.
%J 25th Hawaii Int. Conf. on Sys. Sci.
%K LAllison, CSW, Monash, conf, MolBio, minimum message length encoding, MML,
ML, evolutionary, family, phylogenetic, tree, trees, CSWallace, CSW, human,
Bayesian, finite state, model, machine, FSM, hidden Markov model, primate,
HMM, DNA, multiple alignment, inductive inference, II, bioinformatics, chimp,
HICSS, HICSS25, HICCS92, TR 91 155, TR91-155, TR155, c1992, c199x, c19xx
%V 1
%P 663-674
%M JAN
%D 1992
%O TR 91/155, Dept. Computer Science, Monash University '91
%X A method of Bayesian inference known as MML encoding is applied to inference
of an evolutionary tree and to multiple alignment for K >= 2 strings.
It allows the posterior odds-ratio of two competing hypotheses, for
example two trees, to be calculated. A tree that is a good hypothesis forms
the basis of a short message describing the strings. The mutation
process is modelled by finite-state machine. It is seen that tree
inference and multiple alignment are intimately connected.
[paper],
there is an example on the primate globin pseudo-genes.
(Also see [Bioinformatics].)
%A L. Allison
%A T. I. Dix
%A C. N. Yee
%T Shortest path and closure algorithms for banded matrices.
%J IPL
%V 40
%P 317-322
%M DEC
%D 1991
%O TR 89/133, Computer Science, Monash University, Nov 1989
%K LAllison, Monash, jrnl, all pairs shortest path paths, closure,
band banded matrix matrices, -ve negative cycle problem, constraint,
TR89/133, TR 89, 133, TR133, c1991, c199x, c19xx
%X All pairs shortest paths problem in banded (width b) matrices.
O(n b^2) for entries in band and for negative cycle problem,
O(n^2 b) for all pairs shortest paths where b is band width.
[HTML]
[.ps]
[doi:10.1016/0020-0190(91)90200-2]['07]
%A S. T. S. Ho
%A L. Allison
%A C. N. Yee
%A T. I. Dix
%T Constraint checking for restriction site mapping.
%J 24th Hawaii Int. Conf. on Sys. Sci.
%V ?
%P ???-???
%M JAN
%D 1991
%O TR 89/129 Computer Science, Monash University, JUL '89
%K LAllison, Monash, conf, MolBio, HICSS, HICSS24, HICSS91, bioinformatics,
restriction site mapping, RSM, map, constraint satisfaction problem CSP,
separation theory, TR 89/129 89 129 TR89/129 TR129, c1991, c199x, c19xx
%X also see [Bioinformatics]
%A L. Allison
%A M. R. Garrett
%A A. J. Maeder
%T Particle shape characterization and comparison using curve signatures.
%J 21st Conference of the Fine Particle Society
%M AUG
%D 1990
%C San Diego
%K LAllison, Monash, conf, time warp, warping, shape, particle, closed, curve,
signature, alignment, MML, edit distance, FPS, image processing, IP, c1990
c199x, c19xx
%X also see [MML]
%A S. Ho
%A L. Allison
%A C. N. Yee
%T Restriction site mapping for three or more enzymes.
%J Comp. Appl. BioSci.
%V 6
%N 3
%P 195-204
%M JUL
%D 1990
%O TR 88/117 Dept. Comp. Sci., Monash University Oct '88
%K LAllison, Monash, jrnl, MolBio, CABIOS, RSM, restriction site map, mapping,
constraint satisfaction problem, programming, three, separation theory,
TR 88 117 TR117 TR88/117, c1990, c199x, c19xx, J. Bioinformatics, enzyme
%X "Restriction site mapping requires a generator to put forward possible maps &
a constraint checker to reject false maps. Ideally these combine to give an
algorithm which calculates a sound & complete solution set. 3 algorithms for
generation are presented & compared. Two decompose a multi-enzyme problem
(3+) into subproblems. The constraint checker is based on separation theory.
Some insights into the extent of constraint checking involved in & the
feasibility of more checking for three or more enzymes are discussed. The
trade-off between comp'n time & the soundness of the soln set is examined."
[now J. Bioinformatics]
[jrnl]['07]
also see [Bioinformatics]
%A L. Allison
%A Du Xiaofeng
%T Relating three strings by minimum message length encoding (abstract).
%P 13
%J International Conference on Genes, Proteins and Computers
%W Chester
%I SERC Daresbury Laboratory
%M APR
%D 1990
%K LAllison, Monash, conf, MolBio, multiple, three, triple, alignment,
LCS, LCSS, MML, family, evolutionary phylogenetic tree, bioinformatics,
inductive inference, II, DNA, c1990, c199x, c19xx
%X also see [Bioinformatics]
%A L. Allison
%A C. S. Wallace
%A C. N. Yee
%T Finite-state models in the alignment of macro-molecules.
%J J. Mol. Evol.
%V 35
%N 1
%P 77-89
%M JUL
%D 1992
%K LAllison, jrnl, Monash, MolBio, c1992, c199x, c19xx, TR 90/148, macromolecules,
TR90/148, TR148, 148 inductive inference, II, DNA, bioinformatics, DPA,
dynamic programming algorithm, mutual information, ML, string, sequence,
comparison, alignment, minimum message length encoding, MML, FSM, FSA,
finite state model, analysis, minimum description length, MDL,
Hidden Markov model, HMM, homology, similarity, LCS, LCSS, significance,
evolutionary, edit distance, sequence, r-theory, linear, gap, indel, insert,
delete, Algorithm, Time, Speed, JME, AAAI, Bayes, Bayesian, CSWallace, CSW
%O extended abstract titled:
Inductive inference over macro-molecules
in joint sessions at AAAI Symposium, Stanford MAR 1990
on (i) Artificial Intelligence and Molecular Biology, p5-9
& (ii) Theory and Application of Minimal-Length Encoding, p50-54
also an early version in Technical Report 90/148
Dept. Computer Science, Monash University, Australia 3168
%X MML encoding is a technique of inductive inference with theoretical and
practical advantages. It allows the posterior odds-ratio of two theories
or hypotheses to be calculated. Here it is applied to the problem of
aligning or relating two strings, in particular biological macro-molecules.
We compare the r-theory, that the strings are related, with the null-theory,
that they are not related. If they are related the probabilities of the
various alignments can be calculated. This is done for the one-, three-
and five-state models of relation or mutation. These correspond to linear
and piecewise linear cost functions on runs of indels. We describe how
to estimate the parameters of a model. The validity of a model is itself
a hypothesis and can be tested objectively. This is done on real DNA
and on artificial data. The tests on artificial data indicate limits on
what can be inferred in various situations. The tests on real DNA support
either the three- or the five-state models over the one-state model.
Finally, a fast, approximate minimum message length string comparison
algorithm is described.
[reprint.ps]
[doi:10.1007/BF00160262]['07]
See C. S. Wallace & D.M Boulton
An information measure for classification.
CompJ 11(2) 185-194 Aug '68 (appendix)
for the derivation of the coding scheme for multi-state data.
See also (i) Bishop & Thompson
(ii) Thorne, Kishino & Felsenstein.
[TR90/148](HTML)
[TR90/148](.ps)
[AIMB](.ps)
[Bioinformatics]
%A L. Allison
%A C. S. Wallace
%A C. N. Yee
%T When is a string like a string?
%J International Symposium on Artificial Intelligence and Mathematics.
%W Ft. Lauderdale, Florida, USA
%M JAN
%D 1990
%K LAllison, CSW, CSWallace, Monash, conf, inductive inference, II, homology,
alignment, LCS, edit distance, string, sequence, comparison, similarity,
r-theory, macro-molecule, MolBio, DNA, pattern matching, MML,
minimum message length encoding, AIM AIM90, Hidden Markov model, HMM,
c1990, c199x, c19xx, bioinformatics
%X [paper](HTML)
[paper](.ps)
also see [Bioinformatics]
and [TR90/148](html)
and [TR90/148](.ps)
%A L. Allison
%A C. N. Yee
%T Minimum message length encoding and the comparison of macromolecules.
%J Bulletin of Mathematical Biology
%V 52
%N 3
%M MAY
%D 1990
%P 431-453
%O TR 89/126 Computer Science, Monash University, MAY '89
%K LAllison, Monash, jrnl, minimum message length encoding, MML, c1990, c199x,
c19xx, MDL, ML, inductive inference, II, minimum description length, DNA,
approximate, alignment, similarity, homology, LCS, LCSS, pattern matching,
string, sequence, comparison, Bayesian, Hidden Markov model, HMM,
mutual information, MolBio, bioinformatics, BMB, TR 89/126, 89 126, TR89/126
%X "A method of inductive inference known as minimum message length encoding is
applied to string comparison in molecular biology. The question of whether or
not two strings are related and, if so, of how they are related and the
problem of finding a good theory of string mutation are treated as inductive
inference problems. The method allows the posterior odds-ratio of two string
alignments or of two models of string mutation to be computed. The
connection between models of mutation and existing string alignment
algorithms is made explicit. A fast minimum message length alignment
algorithm is also described."
[doi:10.1007/BF02458580]['06]
Also see [Bioinformatics]
%A C. McDonald
%A L. Allison
%T Denotational semantics of a command interpreter and their implementation
in standard ML.
%J COMPJ
%V 32
%N 5
%P 422-431
%M DEC
%D 1989
%O TR 88/7, Dept. Computer Science, UWA '88
%K LAllison, Monash, COMPJ, jrnl, functional programming, FP, c1989, c198x,
c19xx, shell script, scripting, command language, line, unix,
operating system, OS, denotational semantics, DS, ML, SML, UWA,
TR 88/7, TR88/7, 88 7
%X [paper]
[pdf@compj]['05]
[CSci UWA home][C.M.]
[Denotational Semantics]
%A L. Allison
%T Continuations implement generators and streams.
%J COMPJ
%V 33
%N 5
%P 460-465
%M OCT
%D 1990
%O TR 88/112 Dept. Computer Science, Monash University AUG '88
%K LAllison, Monash, jrnl, continuation, combinator, generator, nondeterminism,
non-determinism, nqueens, n queens, n-queens, source, stream, sink, lazy,
agent, sieve, TR 88/112, TR88/112, TR112, c1988, c198x, c19xx,
functional programming, FP
%X Continuations are used to program non-deterministic generators
and a variation on deterministic stream functions.
Examples include the n-queens problem and the sieve of Eratosthenese.
The continuation style has an imperative flavour but is
functional and free of side effects.
Some (recursive) programs can be executed in constant space.
[paper]
%A L. Allison
%A C. N. Yee
%A M. McGaughey
%T Three-dimensional queens problems.
%R TR 89/130
%I Dept. Computer Science, Monash University, Australia
%M AUG
%D 1989
%K LAllison, Monash, n queens, n-queens, queen, nqueens, chess, 3D,
TR 89 130 TR130 TR89/130, c1989, c198x, c19xx
%X [TR89/130 (HTML)]
[TR89/130 (.ps)]
%A L. Allison
%A C. Y. Yee
%T Restriction site mapping is in separation theory.
%J Comp. Appl. BioSci.
%V 4
%N 1
%P 97-101
%M JAN
%D 1988
%K LAllison, Monash, jrnl, CABIOS, plasmid, map, maps, mapping, combinatorial,
enzyme, restriction site, DNA, phage, RSM, MolBio, separation theory,
linear, constraint, programming, algorithm, inequality, c1988, c198x, c19xx,
J. Bioinformatics, NAR, Nucl. Acids Res. special issue
%X Paper examines constraint checking during backtracking generation
of solutions to the plasmid mapping problem.
The constraints fall into Vaughn Pratt's separation theory.
It is necessary and sufficient to check all simple cycles
in the graph of restriction sites linked by fragments
from the two single digests and the one double digest (can be generalized
to more than two enzymes).
In general, at level 'n' there are at most n new constraints to check.
As an efficiency matter, only those cycles containing 1, 2 or 3
indivisible cycles can be checked which allows a very few false maps to
be generated. This reduces total constraints/map from n*n to 3*n.
[paper (HTML)]
[paper (.ps)]
[Bioinformatics]
[jrnl is now J. Bioinformatics]
%A L. Allison
%T A Practical Introduction to Denotational Semantics.
%S Cambridge Computer Science Texts
%I CUP
%K LAllison, UWA, text, book, textbook, CUP, denotational semantics, DS,
programming language, languages, definition, L4REF, c1986, c198x, c19xx
%D 1986
%X reviewed in Computing Reviews, Nov '87 pp.574-5 and Jul '88 pp.349-350
and in Software P & E 18(5) pp.493-494 May '88
[doi:10.2277/0521314232]['07]
pb, uk us isbn:0521314232, uk us isbn13:978-0521314237;
hb, uk us isbn:0521306892, uk us isbn13:978-0521306898.
[Denotational Semantics]
[book@CUP]
%A L. Allison
%T Direct semantics and exceptions define jumps and coroutines.
%J IPL
%V 31
%N 6
%P 327-330
%M JUN
%D 1989
%O TR 87/90 Dept Computer Science, Monash University, 1987
%K LAllison, Monash, jrnl, denotational semantics, DS, direct, exception,
SML, ML, jump, sequencer, goto, coroutine, control, raise, handle, catch,
throw, IPL, TR 87/90 TR87/90 90 TR90, c1989, c198x, c19xx
%X Direct semantics and continuation semantics are the two main styles of
denotational semantics. Direct semantics is the simpler style but cannot
define the semantics of jumps and other sequencers. This paper shows that,
with the addition of exceptions, direct semantics can define sequencers. In
contrast to the use of continuation semantics, nothing need be added to the
semantics of commands unconnected with sequencers. Since exceptions can be
defined in terms of the lambda-calculus nothing need be added to the
foundations of semantics.
See [TR87/90][HTML]
[doi:10.1016/0020-0190(89)90097-5]['06]
[Denotational Semantics]
%A L. Allison
%T Circular programs and self-referential structures.
%J SPE
%V 19
%N 2
%P 99-109
%M FEB
%D 1989
%O in TR87/91 Dept Computer Science, Monash University, Jan 87
%K jrnl, LAllison, Monash, applicative, functional programming, FP, circular,
recursive, recursion, lazy, evaluation, need, program, list, lists, queue,
doubly linked, tree, trees, breadth first traversal, BFT, threaded, queues,
nub, unique elements, primes, c1989, c198x, c19xx, TR 87/91 TR87/91 TR91 91
%X A circular program creates a data structure whose computation depends on
itself or refers to itself. The technique is used to implement the
classic data structures circular and double-linked lists, threaded trees
and queues in a functional programming language.
[reprint.ps]
[html]
%A L. Allison
%T Some applications of continuations.
%J COMPJ
%K LAllison, Monash, jrnl, functional programming, FP, recursion, backtrack,
backtracking, back track, coroutine, applicative, continuation, parse,
parser, parsing, recursive descent, non-determinism, binary search tree,
merge, combinators, TR 87/91 TR87/91 TR91 91, c1988, c198x, c19xx
%V 31
%N 1
%P 9-11
%M FEB
%D 1988
%O in TR87/91 Dept Computer Science, Monash University, Jan 87
%X [paper]
%A L. Allison
%A T. I. Dix
%T A bit-string longest-common-subsequence algorithm.
%J IPL
%V 23
%M DEC
%D 1986
%P 305-310
%K LAllison, TIDix, Monash, UWA, jrnl, MolBio, LCS, LCSS, c1986, c198x, c19xx,
fast, algorithm, bit string, bit vector, DPA, dynamic programming algorithm,
similarity, homology, bioinformatics, IPL, sequence, approximate, match,
matching, strings, distance, comparison, alignment, speedup, speed
%X Speedup is ~ wordlength (eg. a factor of 32), time is O(n^2/wordlength).
[HTML]
[.ps]
[doi:10.1016/0020-0190(86)90091-8]['07]
%A A. Finlay
%A L. Allison
%T A prescription for Prolog control features based upon denotational semantics
and its implementation in standard ML.
%R TR 87/97
%I Dept. Computer Science, Monash University
%M NOV
%D 1987
%K LAllison, Monash, CSSE, Prolog, logic programming, LP, SML, ML, TR87/97,
TR97 TR 97, c1987, c198x, c19xx, denotational semantics, DS
%X [Logic Programming]
[Denotational Semantics]
%A L. Allison
%T Programming denotational semantics II
%J COMPJ
%V 28
%N 5
%P 480-486
%D 1985
%K COMPJ, jrnl, LAllison, UWA, denotational, DS, continuation semantics,
algol, A68, algol-68, algol68, c1985, c198x, c19xx
%X [paper]
[Denotational Semantics]
%A L. Allison
%T Programming denotational semantics
%J COMPJ
%V 26
%N 2
%P 164-174
%D 1983
%K COMPJ, jrnl, LAllison, UWA, direct, denotational semantics,
DS, Pascal, c1983, c198x, c19xx
%X [paper]
[Denotational Semantics]
%A L. Allison
%T An executable Prolog semantics
%J Algol Bulletin
%N 50
%P 10-18
%M DEC
%D 1983
%K LAllison, UWA, jrnl, denotational semantics, DS, algol, algol-68, algol68,
logic programming, LP, unification, interpreter, Prolog, c1983, c198x, c19xx
%X [doi:10.1145/1061790.1061794]['06] -- AB in ACM digital archive.
Also see [program]
%A L. Allison
%T On syntax directed program editing
%J SPE
%V 13
%P 453-465
%D 1983
%K LAllison, UWA, jrnl, edit, editor, syntax-editor, SED, SDE,
syntax directed, program, structure based, c1983, c198x, c19xx
%X .
%A L. Allison
%T Stable-marriages by coroutines
%J IPL
%V 16
%P 61-65
%M FEB
%D 1983
%K IPL, jrnl, c1983, c198x, c19xx, LAllison, UWA, jrnl, stable marriage,
algorithm, matching, coroutine, modula, two, modula2, modulaII, bipartite,
graph, matching, coroutines
%X The stable marriage problem is an appealing version of many pairing problems.
A solution by coroutines is given, based on the recursive algorithm of
McVitie and Wilson (1971). There are few published algorithms where
coroutines are really useful but they solve this problem very naturally.
[doi:10.1016/0020-0190(83)90025-X]['06]
[LA home]
%A L. Allison
%A P. Edmiston
%T A LOGO survey
%J Australian Computer Bulletin
%V 5
%N 9
%P 40
%M OCT
%D 1981
%K LAllison, UWA, jrnl, logo, programming language,
children school education CAI, c1981, c198x, c19xx
%A L. Allison
%T Generating coset representatives for permutation groups
%J Journal of Algorithms
%V 2
%P 227-244
%M SEP
%D 1981
%K LAllison, UWA, jrnl, coset, group, permutation, backtrack, backtracking,
back track, isomorph rejection, subgroup, generate, representative,
algorithm, c1981, c198x, c19xx
%X .
%A L. Allison
%T Phrase structures, non-determinism and backtracking
%J IPL
%V 7
%N 3
%P 139-143
%M APR
%D 1978
%K LAllison, MelbU, jrnl, grammar, parse, parsing, parser, backtrack,
backtracking, non-determinism, back track, back tracking,
LL, LL1, LL(1), c1978, c197x, c19xx
%X [doi:10.1016/0020-0190(78)90077-7]['06]
[Languages]
%A L. Allison
%A A. Lock
%T A wordprocessor for the handicapped.
%J ACSC 5
%W Perth
%M FEB
%D 1982
%K LAllison, UWA, conf, ACSC5, ACSC82, c1982, c198x, c19xx
%A L. Allison
%A D. Wilde
%T Phrase structures in Pascal.
%J Programming Language Systems
%W Canberra
%P 29-37
%M FEB
%D 1977
%I ANUpress
%K LAllison, MelbU, conf, grammar, parse, parsing, parser, backtrack,
backtracking, back track, c1977, c197x, c19xx, Pascal, LL LL1 LL(1), ACSC,
ACSC0, structure, system
%X essentially ACSC0, conf 24-25 Feb 1977,
volume published in '78, isbn:0708104932.
Also see [Languages]
|
window on the wide world:
|
|