Probabilistic Active Learning with Structure-Sensitive Kernels

Abstract

This work proposes two approaches to improve the poolbased active learning strategy ’Multi-Class Probabilistic Active Learning’ (McPAL) by using two kernel functions based on Gaussian mixture models (GMMs). One uses the kernels for the instance selection of the McPAL strategy, the second employs them in the classification step. The results of the evaluation show that using a different classification model from the one that is used for selection, especially an SVM with one of the kernels, can improve the performance of the active learner in some cases.