Efficient non-parametric function induction in semi-supervised learning

AISTATS 2005 - Proceedings of the 10th International Workshop on Artificial Intelligence and Statistics

Volumn , Issue , 2005, Pages 96-103

  Delalleau, Olivier ^a   Bengio, Yoshua ^a   Le Roux, Nicolas ^a  

^a UNIVERSITÉ DE MONTRÉAL   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

COMPARATIVE EXPERIMENTS; DATA SETS; INDUCTION ALGORITHMS; NON-PARAMETRIC; NON-PARAMETRIC ALGORITHM; PARZEN WINDOWS; SEMI-SUPERVISED LEARNING; TRAINING EXAMPLE; TRAINING PROCESS;

APPROXIMATION ALGORITHMS; ARTIFICIAL INTELLIGENCE; LINEAR SYSTEMS;

SUPERVISED LEARNING;

EID: 84862619225     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (14)

1. Belkin, M., Matveeva, I., & Niyogi, P. (2004). Regularization and semi-supervised learning on large graphs. COLT'2004. Springer.
2. Belkin, M., & Niyogi, P. (2003). Using manifold structure for partially labeled classification. Advances in Neural Information Processing Systems 15. Cambridge, MA: MIT Press.
3. Beyer, K. S., Goldstein, J., Ramakrishnan, R., & Shaft, U. (1999). When is "nearest neighbor" meaningful? Proceeding of the 7th International Conference on Database Theory (pp. 217-235). Springer-Verlag.
4. Chapelle, O., Weston, J., & Scholkopf, B. (2003). Cluster kernels for semi-supervised learning. Advances in Neural Information Processing Systems 15. Cambridge, MA: MIT Press.
5. Kemp, C., Griffiths, T., Stromsten, S., & Tenembaum, J. (2004). Semi-supervised learning with trees. Advances in Neural Information Processing Systems 16. Cambridge, MA: MIT Press.
6. Nadaraya, E. (1964). On estimating regression. Theory of Probability and its Applications, 9, 141-142.
7. Ng, A. Y., Jordan, M. I., & Weiss, Y. (2002). On spectral clustering: analysis and an algorithm. Advances in Neural Information Processing Systems 14. Cambridge, MA: MIT Press.
8. Seeger, M. (2001). Learning with labeled and unlabeled data (Technical Report). Edinburgh University.
9. Szummer, M., & Jaakkola, T. (2002). Partially labeled classification with markov random walks. Advances in Neural Information Processing Systems 14. Cambridge, MA: MIT Press.
10. Watson, G. (1964). Smooth regression analysis. Sankhya - The Indian Journal of Statistics, 26, 359-372.
11. Williams, C. K. I., & Seeger, M. (2001). Using the Nyström method to speed up kernel machines. Advances in Neural Information Processing Systems 13 (pp. 682-688). Cambridge, MA: MIT Press.
12. Zhou, D., Bousquet, O., Navin Lal, T., Weston, J., & Schölkopf, B. (2004). Learning with local and global consistency. Advances in Neural Information Processing Systems 16. Cambridge, MA: MIT Press.
13. Zhu, X., Ghahramani, Z., & Lafferty, J. (2003a). Semi-supervised learning using gaussian fields and harmonic functions. ICML'2003.
14. Zhu, X., Lafferty, J., & Ghahramani, Z. (2003b). Semi-supervised learning: From gaussian fields to gaussian processes (Technical Report CMU-CS-03-175). CMU.