A framework for probability density estimation

Journal of Machine Learning Research

Volumn 2, Issue , 2007, Pages 468-475

  Shawe Taylor, John ^a   Dolia, Alex ^b  

^a UNIVERSITY COLLEGE LONDON   (United Kingdom)

^b UNIVERSITY OF SOUTHAMPTON   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

EXPERIMENTAL EVIDENCE; PROBABILITY DENSITY ESTIMATION;

SOFTWARE ENGINEERING;

ARTIFICIAL INTELLIGENCE;

EID: 77952313702     PISSN: 15324435     EISSN: 15337928     Source Type: Journal    
DOI: None     Document Type: Conference Paper

References (11)

1. Amiran Ambroladze, Emilio Parrado-Hernández, and John Shawe-Taylor. Complexity of pattern classes and Lipschitz property. Theoretical Computer Science, special issue arising from ALT 2005, to appear, 2006.
2. T. Batu, L. Fortnow, R. Rubinfeld, W. D. Smith, and P. White. Testing that distributions are close. In Foundations of Computer Science (FOCS), 2000.
3. S. Ben-David, J. Gehrke, and R. Schuller. A theoretical framework for learning from a pool of disparate data sources. In Proceedings of KDD'02, 2002.
4. S. Ben-David and M. Lindenbaum. Learning distributions by their density levels a paradigm for learning without a teacher. Journal of Computer and Systems Sciences, 55(1):171-182, 1997.
5. Miroslav Dudik, Steve J. Phillips, and Robert E. Schapire. Performance guarantees for regularized maximum entropy density estimation. In Proceedings of the 19th Annual Conference on Learning Theory, pages 123-138, 2006.
6. Sayan Mukherjee and Vladimir Vapnik. Multivariate density estimation: an SVM approach. Technical Report AIM-1653, 1999.
7. B. Schölkopf, J.C. Platt, J.S. Shawe-Taylor, A.J. Smola, and R.C. Williamson. Estimating the support of a high-dimensional distribution. Neural Computation, 13(7):1443-1471, 2001.
8. J. Shawe-Taylor and N. Cristianini. Kernel Methods for Pattern Analysis. Cambridge University Press, Cambridge, UK, 2004.
9. V. Vapnik. The Nature of Statistical Learning Theory. Springer Verlag, New York, 1995.
10. Vladimir Vapnik and Sayan Mukherjee. Support vector method for multivariate density estimation. In Proceedings of the Neural Information Processing Conference (NIPS), pages 659665, 1999.
11. Régis Vert. Theoretical Insights on Density Level Set Estimation with Applications to Anomaly Detection. PhD Thesis, Ecole Normale Superieure, Paris, 2006.