mml-toolbar


Home Introduction The MML Book Fundamentals Applications Bibliography

MML Bibliography





Incomplete, currently Wallace papers only.



[1] L. Allison, C.S. Wallace, and C.N. Yee. Finite-state models in the alignment of macromolecules. Journal of Molecular Evolution, 35:77-89, 1992.
[ bib ]
[2] D. M. Boulton and C. S. Wallace. An information measure for single-link classification. The Computer Journal, 18(3):236-238, August 1975.
[ bib ]
[3] D.M. Boulton and C.S. Wallace. The information content of a multistate distribution. Journal of Theoretical Biology, 23:269-278, 1969.
[ bib ]
[4] D.M. Boulton and C.S. Wallace. A program for numerical classification. Computer Journal, 13:63-69, 1970.
[ bib ]
[5] D.M. Boulton and C.S. Wallace. An information measure for hierarchic classification. The Computer Journal, 16:254-261, 1973.
[ bib ]
[6] D.M. Boulton and C.S. Wallace. A comparison between information measure classification. In Proceedings of ANZAAS Congress, Perth, August 1973.
[ bib ]
[7] D. L. Dowe, L. Allison, T. I. Dix, L. Hunter, C. S. Wallace, and T. Edgoose. Circular clustering of protein dihedral angles by minimum message length. In Pacific Symposium on Biocomputing '96, pages 242-255. World Scientific, 1996.
[ bib | .html ]
[8] D. L. Dowe, J. J. Oliver, and C. S. Wallace. MML estimation of the parameters of the spherical Fisher distribution. In A. Sharma et al., editor, Proc. 7th Conf. Algorithmic Learning Theory (ALT'96), LNAI 1160, pages 213-227, Sydney, Australia, October 1996.
[ bib ]
[9] D. L. Dowe and C. S. Wallace. Resolving the Neyman-Scott problem by Minimum Message Length. In Proc. Computing Science and Statistics - 28th Symposium on the interface, volume 28, pages 614-618, 1997.
[ bib ]
[10] D.L. Dowe, R.A. Baxter, J.J. Oliver, and C.S. Wallace. Point Estimation using the Kullback-Leibler Loss Function and MML. In Proc. 2nd Pacific Asian Conference on Knowledge Discovery and Data Mining (PAKDD'98), pages 87-95, Melbourne, Australia, April 1998. Springer Verlag.
[ bib ]
[11] D.L. Dowe, J.J. Oliver, R.A. Baxter, and C.S. Wallace. Bayesian estimation of the von Mises concentration parameter. Technical report TR 236, Dept. of Computer Science, Monash University, Clayton, Victoria 3168, Australia, 1995.
[ bib ]
[12] D.L. Dowe, J.J. Oliver, R.A. Baxter, and C.S. Wallace. Bayesian estimation of the von Mises concentration parameter. In Proc. 15th Maximum Entropy Conference, Santa Fe, New Mexico, August 1995.
[ bib ]
[13] D.L. Dowe and C.S. Wallace. Resolving the Neyman-Scott problem by Minimum Message Length. Technical report TR no. 97/307, Dept. of Computer Science, Monash University, Clayton, Victoria 3168, Australia, February 1997. Also in Proc. Sydney Int. Stat. Congr. (SISC-96), Sydney, pages 197-198; and in IMS Bulletin (1996), 25 (4), pp410-411.
[ bib ]
[14] D.L. Dowe and C.S. Wallace. Kolmogorov Complexity, Minimum Message Length and Inverse Learning. In Proc. 14th Australian Statistical Conference (ASC-14), page 144, Gold Coast, Qld., Australia, July 1998.
[ bib ]
[15] G. E. Farr and C. S. Wallace. The complexity of Strict Minimum Message Length inference. The Computer Journal, 45(3):285-292, 2002.
[ bib ]
[16] M.P. Georgeff and C.S. Wallace. A general objective for inductive inference. Technical Report 32, Dept of Computer Science, Monash University, Clayton, Victoria 3168, Australia, 1983.
[ bib ]
[17] M.P. Georgeff and C.S. Wallace. A general criterion for inductive inference. In T. O'Shea, editor, Advances in Artificial Intelligence : Proc. Sixth European Conference on Artificial Intelligence, pages 473-482, Amsterdam, 1984. North Holland.
[ bib ]
[18] M.P. Georgeff and C.S. Wallace. A general selection criterion for inductive inference. Technical report TR 44, Dept. of Computer Science, Monash University, Clayton, Victoria 3168, Australia, June 1984.
[ bib ]
[19] M.P. Georgeff and C.S. Wallace. Minimum information estimation of structure. In T. O'Shea, editor, Advances in Artificial Intelligence, pages 219-228. Elsevier, 1985.
[ bib ]
[20] K. B. Korb and C. S. Wallace. In search of the philosopher's stone: Remarks on humphreys and freedman's critique of causal discovery. Brit. J. Phil. Sci., pages 543-553, 1997.
[ bib ]
[21] J. R. Neil, C. S. Wallace, and K. B. Korb. Learning bayesian networks with restricted causal interactions. In Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence (UAI-99), Stockholm, Sweden, July 30-August 1, 1999, pages 486-493, San Francisco, CA, USA, 1999. Morgan Kaufman Publishers.
[ bib ]
[22] J.J. Oliver, D.L. Dowe, and C.S. Wallace. Inferring decision graphs using the minimum message length principle. In A. Adams and L. Sterling, editors, Proceedings of the 5th Australian Joint Conference on Artificial Intelligence, pages 361-367. World Scientific, Singapore, 1992.
[ bib ]
[23] J.D. Patrick and C.S. Wallace. Stone circle geometries: an information theory approach. In D.C. Heggie, editor, Archaeoastronomy in the Old World, pages 231-264. Cambridge University Press, Cambridge, 1982.
[ bib ]
[24] Grace W. Rumantir and Chris S. Wallace. Minimum Message Length criterion for second-order polynomial model selection applied to tropical cyclone intensity forecasting. In Michael R. Berthold et al., editor, Advances in Intelligent Data Analysis V: Fifth International Symposium on Intelligent Data Analysis, IDA (2003), pages 486-496. Springer-Verlag, 2003.
[ bib ]
[25] M. Viswanathan and C. S. Wallace. A note on the comparison of polynomial selection methods. In Proc. 7th Int. Workshop on Artif. Intelligence and Statistics, pages 169-177. Morgan Kaufmann, 1999.
[ bib ]
[26] M. Viswanathan, C.S. Wallace, D.L. Dowe, and K.B. Korb. Finding cutpoints in noisy binary sequences - a revised empirical evaluation. In Proc. 12th Australian Joint Conf. on Artif. Intelligence, Lecture Notes in Artificial Intelligence (LNAI), pages 405-416. ???, 1999.
[ bib ]
[27] C. S. Wallace. Multiple factor analysis by MML estimation. Technical Report 95/218, Dept. of Computer Science, Monash University, Clayton, Victoria 3168, Australia, 1995.
[ bib ]
[28] C. S. Wallace. Statistical and Inductive Inference by Minimum Message Length. Springer, Berlin, Germany, 2005.
[ bib ]
[29] C. S. Wallace and D. M. Boulton. An information measure for classification. Computer Journal, 11:185-194, 1968.
[ bib ]
[30] C. S. Wallace and D. M. Boulton. An invariant Bayes method for point estimation. Classification Society Bulletin, 3(3):11-34, 1975.
[ bib ]
[31] C. S. Wallace and D. L. Dowe. MML estimation of the von Mises concentration parameter. Tech Rept TR 93/193, Dept. of Comp. Sci., Monash Univ., Clayton 3168, Australia, 1993. prov. accepted, Aust. and N.Z. J. Stat.
[ bib ]
[32] C. S. Wallace and D. L. Dowe. Refinements of MDL and MML coding. Computer Journal, 42(4):330-337, 1999. Special issue on Kolmogorov Complexity.
[ bib ]
[33] C. S. Wallace and D. L. Dowe. MML clustering of multi-state, Poisson, von Mises circular and Gaussian distributions. Journal of Statistics and Computing, 10(1):73-83, January 2000.
[ bib ]
[34] C. S. Wallace and P. R. Freeman. Estimation and inference by compact coding. J. Royal Statistical Society B, 49:240-252, 1987.
[ bib ]
[35] C. S. Wallace and P. R. Freeman. Single factor analysis by MML estimation. Journal of the Royal Statistical Society (Series B), 54:195-209, 1992.
[ bib ]
[36] Chris S. Wallace. On the selection of the order of a polynomial model. Technical report, Royal Holloway College, 1997.
[ bib ]
[37] Chris S. Wallace and David M. Boulton. A information measure for classification. The Computer Journal, 11(2):185-194, 1968.
[ bib ]
[38] Chris S. Wallace and David L. Dowe. Minimum message length and Kolmogorov complexity. Computer Journal, 42(4):270-283, 1999. Special issue on Kolmogorov complexity. http://www3.oup.co.uk/computer_journal/hdb/Volume_42/Issue_04/.
[ bib ]
[39] C.S. Wallace. Estimation and inference by compact coding. Technical report TR 46, Dept. of Computer Science, Monash University, Clayton, Victoria 3168, Australia, August 1984.
[ bib ]
[40] C.S. Wallace. An improved program for classification. In Proceedings of the Nineteenth Australian Computer Science Conference (ACSC-9), volume 8, pages 357-366, Monash University, Australia, 1986.
[ bib ]
[41] C.S. Wallace. Classification by Minimum Message Length inference. In G. Goos and J. Hartmanis, editors, Advances in Computing and Information - ICCI '90, pages 72-81. Springer-Verlag, Berlin, 1990.
[ bib ]
[42] C.S. Wallace. False Oracles and SMML Estimators. In Proc. Information, Statistics and Induction in Science conference (ISIS'96), pages 304-316, Singapore, 1996. World Scientific. Was Tech Rept TR 89/128, Monash University, Australia, 1989.
[ bib ]
[43] C.S. Wallace. False Oracles and SMML Estimators. In D.L. Dowe, K.B. Korb, and J.J. Oliver, editors, Proceedings of the Information, Statistics and Induction in Science (ISIS) Conference, pages 304-316, Melbourne, Australia, August 1996. World Scientific. Was Tech Rept 89/128, Dept. Comp. Sci., Monash Univ., Australia, June 1989.
[ bib ]
[44] C.S. Wallace. Intrinsic Classification of Spatially-Correlated Data. Computer Journal, 41(8):602-611, 1998.
[ bib ]
[45] C.S. Wallace and D.L. Dowe. Estimation of the von Mises concentration parameter using Minimum Message Length. In Proc. 12th Australian Statistical Soc. Conf., Monash University, Australia, 1994.
[ bib ]
[46] C.S. Wallace and D.L. Dowe. Intrinsic classification by MML - the Snob program. In C. Zhang and et al., editors, Proc. 7th Australian Joint Conf. on Artif. Intelligence, pages 37-44. World Scientific, Singapore, 1994. See http://www.csse.monash.edu.au/-0.6ex~dld/Snob.html.
[ bib ]
[47] C.S. Wallace and D.L. Dowe. MML mixture modelling of Multi-state, Poisson, von Mises circular and Gaussian distributions. In Proc. Sydney International Statistical Congress (SISC-96), page 197, Sydney, Australia, 1996.
[ bib ]
[48] C.S. Wallace and D.L. Dowe. MML mixture modelling of Multi-state, Poisson, von Mises circular and Gaussian distributions. In Proc. Computing Science and Statistics - 28th Symposium on the interface, volume 28, pages 608-613, 1997.
[ bib ]
[49] C.S. Wallace and D.L. Dowe. MML mixture modelling of Multi-state, Poisson, von Mises circular and Gaussian distributions. In Proc. 6th Int. Workshop on Artif. Intelligence and Statistics, pages 529-536, 1997.
[ bib ]
[50] C.S. Wallace and D.L. Dowe. Minimum Message Length and Kolmogorov Complexity. Computer Journal, 42(4):270-283, 1999. Special issue on Kolmogorov Complexity.
[ bib ]
[51] C.S. Wallace and D.L. Dowe. Rejoinder. Computer Journal, 42(4):345-347, 1999. Special issue on Kolmogorov Complexity.
[ bib ]
[52] C.S. Wallace, K.B. Korb, and H. Dai. Causal discovery via mml. Technical report 254, Dept. of Computer Science, Monash University, Clayton, Victoria 3168, Australia, 1996.
[ bib ]
[53] C.S. Wallace and J.D. Patrick. Coding decision trees. Machine Learning, 11:7-22, 1993.
[ bib ]







Home Introduction The MML Book Fundamentals Applications Bibliography
Artwork and text © 2005 Clayton School of Information Technology, Monash University; All rights reserved.
Recommended browsers - MozillaKonqueror.
$Revision: 1.5 $
Last Updated: Wed Aug 24 15:50:44 EST 2005