Maximum margin semi-supervised learning for structured variables

Advances in Neural Information Processing Systems

Volumn , Issue , 2005, Pages 33-40

  Altun, Y ^a   McAllester, D ^a   Belkin, M ^b  

^a TOYOTA TECHNOLOGICAL INSTITUTE AT CHICAGO   (United States)

^b THE UNIVERSITY OF CHICAGO   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DISCRIMINATIVE APPROACH; INPUT PATTERNS; INTRINSIC GEOMETRY; MAXIMUM MARGIN; RECENT PROGRESS; SEMI-SUPERVISED; SEMI-SUPERVISED LEARNING; SUPERVISED CLASSIFICATION; TEST POINTS; UNLABELED DATA;

SUPERVISED LEARNING;

EID: 33749256006     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (16)

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. Büscher, and T. Scheffer. Multi-view discriminative sequential learning. 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.