The classifiers whose decisions are combined to form the ensemble are called “base classifiers.” This chapter details some of the most popular base classifier models.

Linear and quadratic classifiers are named after the type of discriminant functions they use. Let be the object to classify in one of *c* classes. Let be a vector with coefficients and *w*_{i0} be a constant free term. A linear classifier is any set of *c* linear functions, one for each class, , *i* = 1, …, *c*,

The tag of the largest *g _{i}*(

A straightforward way to train a linear discriminant classifier (LDC) is shown in Figure 2.2 and detailed below:

- Estimate the prior probabilities for the classes. Let
*N*be the number of objects in the data set_{i}**Z**from class ω_{i},*i*= 1, …*c*, and*y*∈ Ω be the class label of_{j}**z**_{j}∈**Z**. Then - Calculate estimates of ...

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