Optimization ClusterSVM Using Improved Nonlinear Kernel
ClusterSVM exploiting the distributional properties of training data accelerates the training process with large-scale data set, and especially benefits two-class problem with a large number of boundary support vectors. The algorithm first partition the training data into disjoint clusters, then train an initial SVM using representatives of these clusters. This initial SVM gives us a global picture of the solution. The initial SVM can approximately identify the support vectors and non-support vectors. The training process is accelerated by replacing non-support vectors with few data. The initial SVM of cluster is the key of training ClusterSVM. This paper proposed an improved nonlinear kernel to generate a nonlinear separating surface which depends on expanding the reduced set incrementally according to information criterion. It uses this kernel to train the initial SVM of clusters. Computational results indicate computational times and the number of training data are much smaller for improved method than that of the conventional ClusterSVM.
Yali Qi Yeli Li Liuping Feng
Beijing Institute of Graphic Communication, Beijing, China, 102600
国际会议
2008 International Conference on Audio，Language and Image Processing(2008国际声音、语言、图像过程大会)
镇江
英文
970-974
2008-07-07（万方平台首次上网日期，不代表论文的发表时间）