Self supervised boosting

Advances in Neural Information Processing Systems

Volumn , Issue , 2003, Pages

  Welling, Max ^a   Zemel, Richard S ^a   Hinton, Geoffrey E ^a  

^a UNIVERSITY OF TORONTO   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

BOLTZMANN EQUATION;

BOLTZMANN DISTRIBUTION; BOOSTING ALGORITHM; CLASSIFICATION PERFORMANCE; CURRENT ESTIMATES; LEARNING PROBLEM; NEGATIVE EXAMPLES; RANDOM FIELD MODEL; SEQUENTIAL APPROACH;

CLASSIFICATION (OF INFORMATION);

EID: 9444254812     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (11)

1. Y. Freund and D. Haussler. Unsupervised learning of distributions of binary vectors using 2-layer networks. In Advances in Neural Information Processing Systems, Volume 4, pages 912-919, 1992.
2. J. Friedman, T. Hastie, and R. Tibshirani. Additive logistic regression: A statistical view of boosting. Technical report, Dept. of Statistics, Stanford University Technical Report., 1998.
3. J.H. Friedman. Greedy function approximation: A gradient boosting machine. Technical report, Technical Report, Dept. of Statistics, Stanford University, 1999.
4. G.E. Hinton. Training products of experts by minimizing contrastive divergence. Neural Computation, 14: 1771-1800, 2002.
5. G.E. Hinton and A. Brown. Spiking Boltzmann machines. In Advances in Neural Information Processing Systems, Volume 12, 2000.
6. G. Lebanon and J. Lafferty. Boosting and maximum likelihood for exponential models. In Advances in Neural Information Processing Systems, Volume 14, 2002.
7. L. Mason, J. Baxter, P. Bartlett, and M. Frean. Boosting algorithms as gradient descent. In Advances in Neural Information Processing Systems, Volume 12, 2000.
8. S. Della Pietra, V.J. Della Pietra, and J.D. Lafferty. Inducing features of random fields. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(4): 380-393, 1997.
9. S. Rosset and E. Segal. Boosting density estimation. In Advances in Neural Information Processing Systems, Volume 15 (this volume), 2002.
10. R.E. Schapire and Y. Singer. Improved boosting algorithms using conti dence-rated predictions. In Computational hearing Theory, pages 80-91, 1998.
11. S.C. Zhu, Z.N. Wu, and D. Mumford. Minimax entropy principle and its application to texture modeling. Neural Computation, 9(8): 1627-1660, 1997.