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Table of Contents Multivariate Data Modeling Classification and Discrimination Introduction | |
See also: linear discriminant analysis, neural networks |
Discrimination techniques are methods which use measured variables to put the observations into different classes. The important difference between multiple regression models and discrimination models is that the dependent variable is some kind of class number. In many cases the objects may be members of only two possible classes, which simplifies the discrimination problem to a binary classification. An example would be a good/bad test in quality inspection.
linear discriminant analysis
(LDA)
k-nearest neighbors
classification
logistic regression
neural network
classifiers
The classification problem can be interpreted in a geometrical way:
find a proper line (or curve) of separation between two or more groups
of similar objects. The type of separating line depends on the method used.
A neural network, for instance, could create a non-linear discriminating
function, whereas the LDA creates only linear surfaces. This of course
implies that not every method is equally well-suited to a particular problem.
The classification problem as shown below may be easily solved by a neural
network while, for example, LDA would fail.
Last Update: 2006-Jän-17