Center-Weighted and Localized Core Vector Machine Algorithm

An improved algorithm for localized support vector machine is proposed to resolve the imbalance of local learning problem in nonlinear classifications on large data sets. The algorithm uses the supervised clustering algorithm for clustering in a feature space of high dimension and then constructs local nonlinear support vector machines for each cluster. According to the geometric feature of irregular borders of enclosing sphere, the geometric center for a stable equilibrium point is constructed and a dual-weighted center of two relevant weights is formed through calculating density center of the cluster. At last, the classification of large data set is carried out by solving the problem of weighted minimum enclosing ball. Compared with the other two algorithms of controlled group, the proposed algorithm shows shorter training time and testing time as well as higher testing precision except for some individual data sets.