Step Function Approximation for Support Vector Reduction

Since introduction, the Support Vector Machines (SVM) has been popularly used in machine learning and data mining tasks due to their strong mathematical background and promising result. Nevertheless, they are noticeably slow in the prediction stage. The speed is influenced by number of support vectors determined in the training phase. Motivated by this fact, several studies are done to reduce the number of support vectors. The reduction should consider the degeneration of learning quality and preserve it at much as possible. Most previous methodologies either reduce the training set or apply a post-processing step to reduce the number of support vectors. In this paper, we proposed a new SVM cost function called Step Regularized Support Vector Machine (SRSVM), which is a standard SVM with extra constrained to reduce the number of support vectors, which can be defined by user. Experimental results are done to evaluate the efficiency and speed of proposed algorithm. SRSVM are also compared to other related SVM algorithms. The comparisons showed that the proposed method is effective in reducing number of support vectors while preserving the high performance of the classifier.