Abstract
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.
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© 2001 Springer Science+Business Media New York
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Hastie, T., Friedman, J., Tibshirani, R. (2001). Linear Methods for Classification. In: The Elements of Statistical Learning. Springer Series in Statistics. Springer, New York, NY. https://doi.org/10.1007/978-0-387-21606-5_4
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DOI: https://doi.org/10.1007/978-0-387-21606-5_4
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4899-0519-2
Online ISBN: 978-0-387-21606-5
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