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Adaptive Regularization for Transductive Support Vector Machine

Z. Xu, R. Jin, J. Zhu, I. King, M. R. Lyu and Z. Yang
Advances in Neural Information Processing Systems 2009
2009, in press


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 regularization 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 adaptive regularization that is data dependant and is based on the smoothness 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.

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  author = {Z. Xu and R. Jin and J. Zhu and I. King and M. R. Lyu and Z. Yang},
  title = {Adaptive Regularization for Transductive Support Vector Machine},
  booktitle = {Advances in Neural Information Processing Systems 2009},
  year = {2009},
  keywords = {semi-supervised learning, Transductive SVMs},
  note = {in press}