Kernel-based hybrid random fields for nonparametric density estimation

Frontiers in Artificial Intelligence and Applications

Volumn 215, Issue , 2010, Pages 427-432

  Freno, Antonino ^a   Trentin, Edmondo ^a   Gori, Marco ^a  

^a UNIVERSITY OF SIENA   (Italy)

Author keywords

[No Author keywords available]

Indexed keywords

GRAPHIC METHODS; LEARNING ALGORITHMS; MAXIMUM LIKELIHOOD ESTIMATION; SPEECH RECOGNITION;

CONDITIONAL DENSITY; DENSITY ESTIMATOR; DISCRETE DOMAINS; GRAPHICAL MODEL; HEURISTICS ALGORITHM; HYBRID RANDOM FIELDS; LIKELIHOOD ESTIMATION; NADARAYA-WATSON KERNEL ESTIMATORS; NONPARAMETRIC DENSITY ESTIMATION; PSEUDO-LIKELIHOOD;

HEURISTIC ALGORITHMS;

EID: 77956014119     PISSN: 09226389     EISSN: 18798314     Source Type: Book Series    
DOI: 10.3233/978-1-60750-606-5-427     Document Type: Conference Paper

References (25)

1. Francis R. Bach and Michael I. Jordan, 'Learning Graphical Models with Mercer Kernels', in Advances in Neural Information Processing Systems, pp. 1009-1016, (2002).
2. Julian Besag, 'Statistical Analysis of Non-Lattice Data', The Statistician, 24, 179-195, (1975).
3. Christopher M. Bishop, Pattern Recognition and Machine Learning, Springer, New York (NY), 2006.
4. Richard O. Duda, Peter E. Hart, and David G. Stork, Pattern Classification, John Wiley & Sons, New York (NY), second edn., 2001.
5. V.A. Epanechnikov, 'Nonparametric Estimation of a Multidimensional Probability Density', Theory of Probability and its Applications, 14, 153-158, (1969).
6. Antonino Freno, Edmondo Trentin, and Marco Gori, 'A Hybrid Random Field Model for Scalable Statistical Learning', Neural Networks, 22, 603-613, (2009).
7. Antonino Freno, Edmondo Trentin, and Marco Gori, 'Scalable Pseudo- Likelihood Estimation in Hybrid Random Fields', in Proceedings of the 15th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2009), eds., J.F. Elder, F. Fogelman-Souli, P. Flach, and M. Zaki, pp. 319-327. ACM, (2009).
8. Jerome Friedman, Trevor Hastie, and Robert Tibshirani, 'Sparse Inverse Covariance Estimation with the Graphical Lasso', Biostatistics, 9, 432-441, (2008).
9. Walter R. Gilks, Sylvia Richardson, and David Spiegelhalter, Markov Chain Monte Carlo in Practice, Chapman & Hall/CRC, 1996.
10. Alexander G. Gray and AndrewW. Moore, ''N-Body' Problems in Statistical Learning', in Advances in Neural Information Processing Systems, pp. 521-527, (2000).
11. Reimar Hofmann and Volker Tresp, 'Discovering Structure in Continous Variables Using Bayesian Networks', in Advances in Neural Information Processing Systems, pp. 500-506, (1995).
12. Reimar Hofmann and Volker Tresp, 'Nonlinear Markov Networks for Continous Variables', in Advances in Neural Information Processing Systems, (1997).
13. Michael P. Holmes, Alexander G. Gray, and Charles L. Isbell, 'Fast Nonparametric Conditional Density Estimation', in Proceedings of the 23rd Conference on Uncertainty in Artificial Intelligence (UAI '07), (2007).
14. J.F. Kenney and E.S. Keeping, Mathematics of Statistics. Part 2, Van Nostrand, Princeton (NJ), second edn., 1951.
15. J.F. Kenney and E.S. Keeping, Mathematics of Statistics. Part 1, Van Nostrand, Princeton (NJ), third edn., 1962.
16. Han Liu, John Lafferty, and Larry Wasserman, 'The Nonparanormal: Semiparametric Estimation of High Dimensional Undirected Graphs', Journal of Machine Learning Research, 10, 2295-2328, (2009).
17. Dimitris Margaritis, 'Distribution-Free Learning of Bayesian Network Structure in Continuous Domains', in AAAI, pp. 825-830, (2005).
18. Elizbar A. Nadaraya, 'On Estimating Regression', Theory of Probability and its Applications, 9, 141-142, (1964).
19. Richard E. Neapolitan, Learning Bayesian Networks, Prentice Hall, Upper Saddle River (NJ), 2004.
20. Emanuel Parzen, 'On Estimation of a Probability Density Function and Mode', Annals of Mathematical Statistics, 33, 1065-1076, (1962).
21. Judea Pearl, Probabilistic Reasoning in Intelligent Systems, Morgan Kaufmann, San Francisco (CA), 1988.
22. 1-regularized MLE', in Advances in Neural Information Processing Systems, pp. 1329-1336, (2008).
23. M. Rosenblatt, 'Conditional Probability Density and Regression Estimators', in Multivariate Analysis, ed., P.R. Krishnaiah, volume II, 25-31, Academic Press, New York, (1969).
24. B.W. Silverman, Density Estimation for Statistics and Data Analysis, Chapman and Hall, 1986.
25. Geoffrey S. Watson, 'Smooth Regression Analysis', Sankhyā: The Indian Journal of Statistics, Series A, 26, 359-372, (1964).