nips nips2005 nips2005-123 nips2005-123-reference knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Y. Altun, D. McAllester, M. Belkin
Abstract: Many real-world classification problems involve the prediction of multiple inter-dependent variables forming some structural dependency. Recent progress in machine learning has mainly focused on supervised classification of such structured variables. In this paper, we investigate structured classification in a semi-supervised setting. We present a discriminative approach that utilizes the intrinsic geometry of input patterns revealed by unlabeled data points and we derive a maximum-margin formulation of semi-supervised learning for structured variables. Unlike transductive algorithms, our formulation naturally extends to new test points. 1
[1] Y. Altun, T. Hofmann, and A. Smola. Gaussian process classification for segmenting and annotating sequences. In ICML, 2004.
[2] Y. Altun, I. Tsochantaridis, and T. Hofmann. Hidden markov support vector machines. In ICML, 2003.
[3] M. Belkin, P. Niyogi, and V. Sindhwani. Manifold regularization: a geometric framework for learning from examples. Technical Report 06, UChicago CS, 2004.
[4] Avrim Blum and Tom Mitchell. Combining labeled and unlabeled data with cotraining. In COLT, 1998.
[5] U. Brefeld, C. B¨scher, and T. Scheffer. Multi-view discriminative sequential learning. u In (ECML), 2005.
[6] O. Chappelle, J. Weston, and B. Scholkopf. Cluster kernels for semi-supervised learning. In (NIPS), 2002.
[7] M. Collins and N.l Duffy. Convolution kernels for natural language. In (NIPS), 2001.
[8] Olivier Delalleau, Yoshua Bengio, and Nicolas Le Roux. Efficient non-parametric function induction in semi-supervised learning. In Proceedings of AISTAT, 2005.
[9] Thorsten Joachims. Transductive inference for text classification using support vector machines. In (ICML), pages 200–209, 1999.
[10] G. Kimeldorf and G. Wahba. Some results on tchebychean spline functions. Journal of Mathematics Analysis and Applications, 33:82–95, 1971.
[11] John Lafferty, Yan Liu, and Xiaojin Zhu. Kernel conditional random fields: Representation, clique selection, and semi-supervised learning. In (ICML), 2004.
[12] K. Nigam, A. K. McCallum, S. Thrun, and T. M. Mitchell. Learning to classify text from labeled and unlabeled documents. In Proceedings of AAAI-98, pages 792–799, Madison, US, 1998.
[13] V. Sindhwani, P. Niyogi, and M. Belkin. Beyond the point cloud: from transductive to semi-supervised learning. In (ICML), 2005.
[14] B. Taskar, C. Guestrin, and D. Koller. Max-margin markov networks. In NIPS, 2004.
[15] I. Tsochantaridis, T. Hofmann, T. Joachims, and Y. Altun. Support vector machine learning for interdependent and structured output spaces. In (ICML), 2004.
[16] T. Zhang. personal communication.