Density Weighted Core Support Vector Machine

Core Vector Machine (CVM) can be used to deal with large data sets classification problem, but CVM do not consider the density distribution of the data. In order to obtain the optimal description of the data, we propose a density weighted core support vector machine (DWCVM). In the proposed DWCVM, the relative density of each data point is based on the density distribution of the target data using the k -nearest neighbor ( k -NN) approach. Experimental results on several benchmark data sets show that the performance of DWCVM is much better than CVM.