A Modified Incremental Support Vector Machine for Regression

Abstract

support vector machine (SVM) has been shown to exhibit superior predictive power compared to traditional approaches in many studies, such as mechanical equipment monitoring and diagnosis. However, SVM training is very costly in terms of time and memory consumption due to the enormous amounts of training data and the quadratic programming problem. In order to improve SVM training speed and accuracy, we propose a modified incremental support vector machine (MISVM) for regression problems in this paper. The main concepts are that using the distance from the margin vectors which violate the Karush-Kuhn-Tucker (KKT) condition to the final decision hyperplane to evaluate the importance of each margin vectors, and the margin vectors whose distance is below the specified value are preserved, the others are eliminated. Then the original SVs and the remaining margin vectors are used to train a new SVM. The proposed MISVM can not only eliminate the unimportant samples such as noise samples, but also preserved the important samples. The effectiveness of the proposed MISVMs is demonstrated with two UCI data sets. These experiments also show that the proposed MISVM is competitive with previously published methods.