Adaptive Regularization for Transductive Support Vector Machine
Abstract
We discuss the framework of Transductive Support Vector Machine (TSVM) from the perspective of the regularization strength induced by the unlabeled data. In this framework, SVM and TSVM can be regarded as a learning machine without regularization and one with full regular-ization from the unlabeled data, respectively. Therefore, to supplement this framework of the regularization strength, it is necessary to introduce data-dependant partial regularization. To this end, we reformulate TSVM into a form with controllable regularization strength, which includes SVM and TSVM as special cases. Furthermore, we introduce a method of adap-tive regularization that is data dependant and is based on the smooth-ness assumption. Experiments on a set of benchmark data sets indicate the promising results of the proposed work compared with state-of-the-art TSVM algorithms. 1
Authors: Zenglin Xu, Rong Jin, Jianke Zhu, Irwin King, Michael Rung-Tsong Lyu, Zhirong Yang