Abstract: The proximal support vector machines (PSVM) is a regularized least squares problem, which has an analytic solution. However, the PSVM lacks sparseness, and all training dates become support vectors. I}his paper focused on ef- fectively controling the sparseness of the PSVM. An incremental density weighted proximal support vector machine (IDWPSVM) was proposed, which selects the basis support vectors in the training set, The experiment results show that the accuracy of the IDWPSVM can is similar with the SVM, PSVM and DWPSVM methods, and convergence speed is faster. The )DWPSVM can effectively control the sparseness of the PSVM.

Key words: Proximal support vector machine, Density weight, Increment, Sparseness