Supervised Classification

LA home
Computing
 Algorithms
 Bioinformatics
 FP,  λ
 Logic,  π
 MML
 Prog.Langs

MML
 Structured
  mixtures
  HMM
  DTree
  DGraph
  Supervised
  Unsupervised
  Supervised

For multivariate data, a classification function predicts one (or more) output attribute(s) (dependent variable(s)) given the values of the input attributes. Depending on usage, the prediction can be "definite" or probabilistic over possible values.

A classification function is learned from, or fitted to, training data. It is then tested on (surprise) test data. Over-fitting is a risk - where the model fits both the structure and the noise in the training data. Techniques such as cross-validation can be used to provide a stopping criterion. Minimum message length (MML) inference has a natural stopping criterion and is generally resistant to over-fitting

The output attribute, its range of values, and the training data are given - hence `supervised classification'.

Examples of classes of classification (decision-) functions:

window on the wide world:

Computer Science Education Week

Linux
 Ubuntu
free op. sys.
OpenOffice
free office suite,
ver 3.4+

The GIMP
~ free photoshop
Firefox
web browser
FlashBlock
like it says!

© L. Allison   http://www.allisons.org/ll/   (or as otherwise indicated),
Faculty of Information Technology (Clayton), Monash University, Australia 3800 (6/'05 was School of Computer Science and Software Engineering, Fac. Info. Tech., Monash University,
was Department of Computer Science, Fac. Comp. & Info. Tech., '89 was Department of Computer Science, Fac. Sci., '68-'71 was Department of Information Science, Fac. Sci.)
Created with "vi (Linux + Solaris)",  charset=iso-8859-1,  fetched Sunday, 20-Apr-2014 03:04:53 EST.