Fast weight calculation for kernel-based perceptron in two-class classification problems
We propose a method, called Direct Kernel Perceptron (DKP), to directly calculate the weights of a single perceptron using a closed-form expression which does not require any training stage. The weigths minimize a performance measure which simultaneously takes into account the training error and the classification margin of the perceptron. The ability to learn non-linearly separable problems is provided by a kernel mapping between the input and the hidden space. Using Gaussian kernels, DKP achieves better results than the standard Support Vector Machine (SVM) and Linear Discriminant Analysis (LDA) for a wide variety of benchmark two-class data sets. The computational cost of DKP linearly increases with the dimension of the input space and it is much lower than the corresponding to SVM.
keywords: neuralcomputing
Publication: Congress
1624015008783
June 18, 2021
/research/publications/fast-weight-calculation-for-kernel-based-perceptron-in-two-class-classification-problems
We propose a method, called Direct Kernel Perceptron (DKP), to directly calculate the weights of a single perceptron using a closed-form expression which does not require any training stage. The weigths minimize a performance measure which simultaneously takes into account the training error and the classification margin of the perceptron. The ability to learn non-linearly separable problems is provided by a kernel mapping between the input and the hidden space. Using Gaussian kernels, DKP achieves better results than the standard Support Vector Machine (SVM) and Linear Discriminant Analysis (LDA) for a wide variety of benchmark two-class data sets. The computational cost of DKP linearly increases with the dimension of the input space and it is much lower than the corresponding to SVM. - M.Fernández-Delgado, J.Ribeiro, E.Cernadas, S.Barro - 10.1109/IJCNN.2010.5596844
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