Least Squares Support Vector Machine classifiers using PCNNs
Yongsheng Sang, Haixian Zhang, Lin Zuo · 2008
Least Squares Support Vector Machine (LS-SVM) is a modified version of traditional Support Vector Machine (SVM). LS-SVM considers equality constraints, therefore it solves a set of linear equations instead of quadratic programming problem in SVM. However, the sparseness of LS-SVM is lost due to it’s ∈-sensitive cost function. Sparseness can be obtained by applying a pruning method, which eliminates some vectors with smallest support values and retrains the remaining samples. But iterative retraining is a time-consuming process. Motivated by the fact that boundary samples are more significant for constructing a LS-SVM classifier, this paper proposes a method of using Pulse Coupled Neural Networks (PCNNs) to search boundary samples of original data sets. The original data sets are mapped into some PCNN neurons, and a firing algorithm is designed to determine which samples lie at boundary region. It gives a novel approach to impose sparsity for LS-SVM. Experiments show that the proposed method can effectively detect boundary samples and speed up LS-SVM classifiers.