On-line confidence machines are well-calibrated

Annual Symposium on Foundations of Computer Science - Proceedings

Volumn , Issue , 2002, Pages 187-196

  Vovk, Vladimir ^a  

^a UNIVERSITY OF LONDON   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

ASYMPTOTIC STABILITY; COMPUTATIONAL METHODS; ERROR ANALYSIS; LEARNING ALGORITHMS; ONLINE SYSTEMS; PROBABILITY; RANDOM PROCESSES;

INDUCTIVE CONFIDENCE MACHINE; TRANSDUCTIVE CONFIDENCE MACHINE;

LEARNING SYSTEMS;

EID: 0036953613     PISSN: 02725428     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (15)

1. T.H. Cormen, C.E. Leiserson, and R.L. Rivest. Introduction to Algorithms. MIT Press, Cambridge, MA, 1990.
2. L. Devroye, L. Györfi, and G. Lugosi, Probabilistic Theory of Pattern Recognition. Springer, New York, 1996.
3. T. Melluish, C. Saunders, I. Nouretdinov, and V. Vovk. Comparing the Bayes and typicalness frameworks. In L. De Raedt and P. Flash, editors, Machine Learning: ECML 2001. Proceedings of the 12th European Conference on Machine Learning, volume 2167 of Lecture Notes in Artificial Intelligence, pages 360-371. Springer 2001.
4. I. Nouretdinov, T. Melluish, and V. Vovk. Ridge Regression Confidence Machine. In Proceedings of the 18th International Conference on Machine Learning, pages 385-392, San Francisco, CA, 2001, Morgan Kaufmann.
5. I. Nouretdinov, V. Vovk, M. Vyugin, and A. Gammerman. Pattern recognition and density estimation under the general i.i.d. assumption. In D. Helmbold and B. Williamson, editors, Proceedings of the 14th Annual Conference on Computational Learning Theory and 5th European Conference on Computational Learning Theory, volume 2111 of Lecture Notes in Artificial Intelligence, pages 337-353. Springer, 2001.
6. H. Papadopoulos, V. Vovk, and A. Gammerman. Qualified predictions for large data sets in the case of pattern recognition. In Proceedings of the International Conference on Machine Learning and Applications (ICMLA'02), pages 159-163, CSREA Press, 2002.
7. K. Proedrou, I. Nouretdinov, V. Vovk, and A. Gammerman. Transductive confidence machines for pattern recognition. In Proceedings of the 13th European Conference on Machine Learning, 2002.
8. C. Saunders, A. Gammerman, and V. Vovk, Transduction with confidence and credibility. In Proceedings of the 16th International Joint Conference on Artificial Intelligence, pages 722-726, 1999.
9. M.J. Schervish. Theory of Statistics. Springer, New York, 1995.
10. A. N. Shiryaev. Probability. Springer, New York, second edition, 1996.
11. L. G. Valiant. A theory of the learnable. Communications of the ACM, 27:1134-1142, 1984.
12. V.N. Vapnik, Statistical Learning Theory. Wiley, New York, 1998.
13. V. Vovk. Asymptotic optimality of Transductive Confidence Machine. In Proceedings of the 13th International Conference on Algorithmic Learning Theory, 2002.
14. V. Vovk. Towards a universal well-calibrated algorithm for on-line classification. Technical Report CLRC-TR-02-03, Computer Learning Research Centre, Department of Computer Science, Royal Holloway, University of London, http://www.clrc.rhul.ac.uk, August 2002.
15. V. Vovk, A. Gammerman, and C. Saunders, Machine-learning applications of algorithmic randomness. In Proceedings of the 16th International Conference on Machine Learning, pages 444-453, San Francisco, CA, 1999. Morgan Kaufmann.