Support vector pursuit learning

The proposed SVPL uses an incremental updating model similar to standard SVM to update the trained SVM parameters and provides the same learning performance as that obtained by batch learning, but is faster than other methods.

Abstract

In many practical situations in support vector machine learning, it is often expected to further improve the generalization capability after the learning process has been completed. One of the common approaches is to add training data to the support vector machine (SVM) and retrain SVM, but retraining for each new data point or data set can be very expensive. In view of the learning method of human beings, it seems natural to build posterior learning results upon prior results. In this paper, we propose an incremental batch training method called support vector pursuit learning (SVPL). The SVPL uses an incremental updating model similar to standard SVM to update the trained SVM parameters. SVPL provides the same learning performance as that obtained by batch learning, but is faster than other methods. The effectiveness of the presented method is demonstrated through experiments.