Computing the Bayes Kernel Classifier
Pál Ruján, Mario Marchand · The MIT Press eBooks · 2000
Introduction Support Vector Machines try to achieve good generalization by computing the maximum margin separating hyperplane in a high-dimensional feature space. This approach eectively combines two very good ideas. The rst idea is to map the space of input vectors into a very high-dimensional feature space in such a way that nonlinear decisions functions on the input space can be constructed by using only separating hyperplanes on the feature space. By making use of kernels, we can implicitly perform such mappings without explicitly using high-dimensional separating vectors(Boser et al., 1992). Since it is very likely that the training examples will be linearly separable in the high-dimensional feature space, this method oers an elegant alternative to network growth algorithms as in(Rujan and Marchand, 1989; Marchand et al., 1990) which try to construct nonlinear decision surfaces by combining perceptrons. The second idea is to construct the separating hyperplane on th