Supervised Classification

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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:

Coding Ockham's Razor, L. Allison, Springer

A Practical Introduction to Denotational Semantics, L. Allison, CUP

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© L. Allison   (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.)
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