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- W3023951744 endingPage "211" @default.
- W3023951744 startingPage "121" @default.
- W3023951744 abstract "This chapter deals with the design of linear classifiers described by linear discriminant functions (hyperplanes) g(x). In the simple two-class case, it is seen that the perceptron algorithm computes the weights of the linear function g (x), provided that the classes are linearly separable. For nonlinearly separable classes linear classifiers were optimally designed, for example, by minimizing the squared error. The chapter discusses the problems that are not linearly separable and for which the design of a linear classifier, even in an optimal way, does not lead to satisfactory performance. The design of nonlinear classifiers emerges now as an unescapable necessity. To seek nonlinearly separable problems one does not need to go into complicated situations. The well-known Exclusive OR (XOR) Boolean function is a typical example of such a problem. Boolean functions are interpreted as classification tasks. The major concern of this chapter is to tackle the XOR problem and then to extend the procedure to more general cases of nonlinearly separable classes." @default.
- W3023951744 created "2020-05-13" @default.
- W3023951744 creator A5085953846 @default.
- W3023951744 date "2006-01-01" @default.
- W3023951744 modified "2023-09-27" @default.
- W3023951744 title "Nonlinear Classifiers" @default.
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