Supervised Classification

LA home
Computing
MML
 Glossary
 Structured
  Mixtures
  HMM
  DTree
  DGraph
  Supervised
  Unsupervised
  Graphs
  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:


(Also see unsupervised learning.)

www #ad:

↑ © L. Allison, www.allisons.org/ll/   (or as otherwise indicated).
Created with "vi (Linux)",  charset=iso-8859-1,   fetched Friday, 26-Nov-2021 23:42:45 EST.

Free: Linux, Ubuntu operating-sys, OpenOffice office-suite, The GIMP ~photoshop, Firefox web-browser, FlashBlock flash on/off.