Large-scale sparsified manifold regularization

Advances in Neural Information Processing Systems

Volumn , Issue , 2007, Pages 1401-1408

  Tsang, Ivor W ^a   Kwok, James T ^a  

^a HONG KONG UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Hong Kong)

Author keywords

[No Author keywords available]

Indexed keywords

KERNEL EXPANSION; KERNEL METHODS; LABELED AND UNLABELED DATA; LAPLACIANS; MINIMUM ENCLOSING BALL; REGULARIZATION FRAMEWORK; REGULARIZER; SEMI-SUPERVISED LEARNING; SEMI-SUPERVISED LEARNING METHODS; SPACE COMPLEXITY; SPARSE SOLUTIONS; STATE-OF-THE-ART PERFORMANCE; VECTOR MACHINES;

SUPERVISED LEARNING;

EID: 84864041407     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (15)

1. M. Belkin, P. Niyogi, and V. Sindhwani. Manifold regularization: A geometric framework for learning from labeled and unlabeled examples. Journal of Machine Learning Research, 7:2399-2434, 2006.
2. O. Chapelle, B. Schölkopf, and A. Zien. Semi-Supervised Learning. MIT Press, Cambridge, MA, USA, 2006.
3. O. Delalleau, Y. Bengio, and N. L. Roux. Efficient non-parametric function induction in semi-supervised learning. In Proceedings of the Tenth International Workshop on Artificial Intelligence and Statistics, Barbados, January 2005.
4. G. Desaulniers, J. Desrosiers, and M.M. Solomon. Column Generation. Springer, 2005.
5. J. Garcke and M. Griebel. Semi-supervised learning with sparse grids. In Proceedings of the ICML Workshop on Learning with Partially Classified Training Data, Bonn, Germany, August 2005.
6. T. Gärtner, Q.V. Le, S. Burton, A. Smola, and S.V.N. Vishwanathan. Large-scale multiclass transduction. In Y. Weiss, B. Schölkopf, and J. Platt, editors, Advances in Neural Information Processing Systems 18. MIT Press, Cambridge, MA, 2006.
7. F. Girosi. An equivalence between sparse approximation and support vector machines. Neural Computation, 10(6):1455-1480, 1998.
8. Y.-J. Lee and O.L. Mangasarian. RSVM: Reduced support vector machines. In Proceeding of the First SIAM International Conference on Data Mining, 2001.
9. V. Sindhwani, M. Belkin, and P. Niyogi. The geometric basis of semi-supervised learning. In Semisupervised Learning. MIT Press, 2005.
10. V. Sindhwani, P. Niyogi, and M. Belkin. Beyond the point cloud: from transductive to semi-supervised learning. In Proceedings of the Twenty-Second International Conference on Machine Learning, pages 825-832, Bonn, Germany, August 2005.
11. R. Tibshirani. Regression shrinkage and selection via the Lasso. Journal of the Royal Statistical Society: Series B, 58:267-288, 1996.
12. I. W. Tsang, J. T. Kwok, and P.-M. Cheung. Core vector machines: Fast SVM training on very large data sets. Journal of Machine Learning Research, 6:363-392, 2005.
13. I. W. Tsang, J. T. Kwok, and K. T. Lai. Core vector regression for very large regression problems. In Proceedings of the Twenty-Second International Conference on Machine Learning, pages 913-920, Bonn, Germany, August 2005.
14. X. Zhu. Semi-supervised learning literature survey. Technical Report 1530, Department of Computer Sciences, University of Wisconsin - Madison, 2005.
15. X. Zhu and J. Lafferty. Harmonic mixtures: Combining mixture models and graph-based methods. In Proceedings of the Twenty-Second International Conference on Machine Learning, Bonn, Germany, August 2005.