Two-Phased Active Support Kernel Machine Learning

Abstract

Since SVMs have met with significant success in numerous real-world learning, SVM-based active learning has been proposed in the active learning context and it has been successfully applied in the domains like document classification, in which SVMs using linear kernel are known to be effective for the task. However, it is difficult to apply SVM-based active learning to general domains because the kernel used in SVMs should be selected properly before the active learning process but good kernels for the target task is usually unknown. If the pre-selected kernel is inadequate for the target data, both the active learning process and the learned SVM have poor performance. Therefore, new active learning methods are required which effectively find an adequate kernel for the target data as well as the labels of unknown samples in the active learning process.

In this paper, we propose a two-phased SKM-based active learning method for the purpose. By experiments, we show that the proposed SKM-based active learning method has quick response suited to interaction with human experts and can find an appropriate kernel among linear combinations of given multiple kernels.