September 2004
Intermediate to advanced
496 pages
13h 57m
English
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4.4. Linear SVMs: Fuzzy Separation
Sometimes the training data points are not clearly separable, and most often, they are best characterized as "fuzzily" separable. It is therefore convenient to introduce the notion of a fuzzy (or soft) decision region to cope with such situations. For the nonseparable cases, it is not possible to construct a linear hyperplane decision boundary without incurring classification errors. A fuzzy SVM is a model that allows a more relaxed separation, which in turn would facilitate a more robust decision.
For fuzzy SVM classifiers, it is no longer suitable to talk about the separation width. Instead, it is more appropriate to adopt a notion of fuzzy region or, more exactly, fuzzy separation region. Accordingly, the ...
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