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Erschienen in:
2009 | OriginalPaper | Buchkapitel
Linear Methods for Classification
verfasst von : Trevor Hastie, Robert Tibshirani, Jerome Friedman
Erschienen in: The Elements of Statistical Learning
Verlag: Springer New York
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In this chapter we revisit the classification problem and focus on linear methods for classification. Since our predictor
G
(
x
) takes values in a discrete set
G
, we can always divide the input space into a collection of regions labeled according to the classification.We saw in Chapter 2 that the boundaries of these regions can be rough or smooth, depending on the prediction function. For an important class of procedures, these
decision boundaries
are linear; this is what we will mean by linear methods for classification.