Probability product kernels

Journal of Machine Learning Research

Volumn 5, Issue , 2004, Pages 819-844

  Jebara, Tony ^a   Kondor, Risi ^a   Howard, Andrew ^a  

^a Fu Foundation School of Engineering and Applied Science   (United States)

Author keywords

Bhattacharyya affinity; Dynamical systems; Expected likelihood; Exponential family; Generative models; Graphical models; Hellinger divergence; Hidden Markov models; Kernels; Kullback leibler divergence; Latent models; Mean field; Support vector machines

Indexed keywords

ALGORITHMS; DYNAMICAL SYSTEMS; LINEAR CONTROL SYSTEMS; MARKOV PROCESSES; PROBABILITY DISTRIBUTIONS; SUPPORT VECTOR MACHINES;

BHATTACHARYYA AFFINITY; EXPECTED LIKELIHOODS; EXPONENTIAL FAMILY; GENERATIVE MODEL; GRAPHICAL MODEL; HELLINGER DIVERGENCE; KERNELS; KULLBACK LEIBLER DIVERGENCE; LATENT MODELS; MEAN FIELD;

HIDDEN MARKOV MODELS;

EID: 18544365764     PISSN: 15324435     EISSN: 15337928     Source Type: Journal    
DOI: None     Document Type: Article

References (35)

1. Y. Bengio and P. Frasconi. Input-output HMM's for sequence processing. IEEE Transactions on Neural Networks, 7(5):1231-1249, September 1996.
2. A. Bhattacharyya. On a measure of divergence between two statistical populations defined by their probability distributions. Bull. Calcutta Math Soc., 1943.
3. M. Collins and N. Duffy. Convolution kernels for natural language. In Neural Information Processing Systems 14, 2002.
4. C. Cortes, P. Haffner, and M. Mohri. Rational kernels. In Neural Information Processing Systems 15, 2002.
5. D. R. Cutting, D. R. Karger, J. O. Pederson, and K. J. W. Tukey. Scatter/gather: a cluster-based approach to browsing large document collections. In Proceedings ACM/SIGIR, 1992.
6. R. Davidson and J. MacKinnon. Estimation and Inference in Econometrics. Oxford University Press, 1993.
7. Z. Ghahramani and M. Jordan. Factorial hidden Markov models. Machine Learning, 29:245-273, 1997.
8. M. Goldzmidt and M. Sahami. A probabilistic approach to full-text document clustering. Technical report, Stanford University, 1998. Database Group Publication 60.
9. T. Hastie, R. Tibshirani, and Friedman J. H. The Elements of Statistical Learning. Springer Verlag, 2001.
10. D. Haussler. Convolution kernels on discrete structures. Technical Report UCSC-CRL-99-10, University of California at Santa Cruz, 1999.
11. T. Jaakkola. Tutorial on variational approximation methods. In Advanced Mean Field Methods: Theory and Practice. MIT Press, 2000.
12. T. Jaakkola and D. Haussler. Exploiting generative models in discriminative classifiers. In Neural Information Processing Systems 11, 1998.
13. T. Jaakkola, M. Meila, and T. Jebara. Maximum entropy discrimination. In Neural Information Processing Systems 12, 1999.
14. T. Jebara and R. Kondor. Bhattacharyya and expected likelihood kernels. In Conference on Learning Theory, 2003.
15. T. Joachims, N. Cristianini, and J. Shawe-Taylor. Composite kernels for hypertext categorisation. In International Conference on Machine Learning, 2001.
16. M. Jordan and C. Bishop. Introduction to Graphical Models. In progress, 2004.
17. H. Kashima, K. Tsuda, and A. Inokuchi. Marginalized kernels between labeled graphs. In Machine Learning: Tenth International Conference, ICML, 2003.
18. R. Kondor and T. Jebara. A kernel between sets of vectors. In Machine Learning: Tenth International Conference, 2003.
19. J. Lafferty and G. Lebanon. Information diffusion kernels. In Neural Information Processing Systems, 2002.
20. C. Leslie, E. Eskin, J. Weston, and W. S. Noble. Mismatch string kernels for SVM protein classification. In Neural Information Processing Systems, 2002.
21. P. J. Moreno, P. P. Ho, and N. Vasconcelos. A Kullback-Leibler divergence based kernel for SVM classification in multimedia applications. In Neural Information Processing Systems, 2004.
22. C. Ong, A. Smola, and R. Williamson. Superkernels. In Neural Information Processing Systems, 2002.
23. C. S. Ong, M. Xavier, S. Canu, and A. J. Smola. Learning with non-positive kernels. In ICML, 2004.
24. V. Pavlovic, J. M. Rehg, and J. MacCormick. Learning switching linear models of human motion. In Neural Information Processing Systems 13, pages 981-987, 2000.
25. J. Pearl. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann, 1997.
26. J. Platt. Fast training of support vector machines using sequential minimal optimization. In B. Schölkopf, C. Burges, and A. Smola, editors, Advances in Kernel Methods - Support Vector Learning. MIT Press, 1999.
27. B. Schölkopf and A. J. Smola. Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. MIT Press, 2001.
28. B. Schölkopf and A. J. Smola. Learning with Kernels. MIT Press, 2002.
29. R. H. Shumway and D. S. Stoffer. An approach to time series smoothing and forecasting using the EM algorithm. J. of Time Series Analysis, 3(4):253-264, 1982.
30. B. W. Silverman. Density Estimation for Statistics and Data Analysis. Chapman and Hall: London., 1986.
31. F. Topsoe. Some inequalities for information divergence and related measures of discrimination. J. of Inequalities in Pure and Applied Mathematics, 2(1), 1999.
32. K. Tsuda, T. Kin, and K. Asai. Marginalized kernels for biological sequences. Bioinformatics, 18(90001):S268-S275, 2002.
33. V. Vapnik. Statistical Learning Theory. John Wiley & Sons, 1998.
34. S. V. N. Vishawanathan and A. J. Smola. Fast kernels for string and tree matching. In Neural Information Processing Systems 15, 2002.
35. C. Watkins. Advances in kernel methods, chapter Dynamic Alignment Kernels. MIT Press, 2000.