^up^ [01] >>

**Problem**:
Given multivariate data, i.e. a number of things,
drawn from a sample space T×U,
learn a function in T->U,

Called "supervised" because examples are given (e.g. by a supervisor or expert) and the aim is to infer a function to mimic the given behaviour.

**Supervised Classification**
is a special case of the above where U is a discrete
attribute often called "the class".

This document is online at http://www.csse.monash.edu.au/~lloyd/Archive/2005-07-Supervised/index.shtml and contains hyper-links to other resources.

<< [02] >>

- Given {(x
_{i}, y_{i})} fit a function from some class (e.g. polynomial) to predict y given x.

(e.g. Usually called polynomial regression.)

- Time-series:
Predict x
_{i+1}given {x_{j}| j<i}.

- Supervised Classification . . .

<< [03] >>

**Training Set**: A data set on
which to learn the model.

**Test Set**: A data set given after learning,
to test the accuracy of predictions.

**Overfitting**:
When a too complex model is learned -
often revealed when errors on the test set
are much worse than those on the training set.

<< [04] >>

Supervised classification is a special case of supervised learning where the output-space, U, is a single discrete attribute often called "the class".

(cf Unsupervised classification where the number of classes, their spec's and memberships are all unknown.)

Example model classes:**decision tree**(properly*classification tree*)

- decison (classification) graph, forest etc.

- artificial neural network (ANN) (one output node per class)

<< [05] >>

Now see decision trees

© L. Allison, School of Computer Science and Software Engineering, Monash University, Australia 3800. Created with "vi (IRIX)", charset=iso-8859-1