Reductive and effective discriminative information-based nonparallel support vector machine

In the paper, to improve the performance of discriminative information-based nonparallel support vector machine (DINPSVM), we propose a novel algorithm called reductive and effective discriminative information-based nonparallel support vector machine (REDINPSVM). First, we introduce the regularization term to achieve the structural risk minimization principle. This embodies the marrow of statistical learning theory, so this modification can enhance the generalization ability of classification algorithms. Second, we apply the k-nearest neighbor method to eliminate some redundant constraints that would cut down on time complexity. Finally, to accelerate the computation, we introduce the least squares technique to solve two systems of linear equations. Comprehensive experimental results on twenty-three UCI benchmark datasets and six Image datasets demonstrate the validity of the proposed method.

Automated summary

In this paper, a novel algorithm called REDINPSVM is proposed to improve the performance of DINPSVM. The algorithm introduces a regularization term to achieve structural risk minimization, applies the k-nearest neighbor method to eliminate some redundant constraints, and uses the least squares technique to accelerate computation. Comprehensive experimental results on various datasets demonstrate the validity of the proposed method.