Probabilities of discrepancy between minima of cross-validation, Vapnik bounds and true risks

• Autorzy: Przemysław Klęsk
• Języki publikacji: EN

Abstrakty

EN

Two known approaches to complexity selection are taken under consideration: n-fold cross-validation and structural risk minimization. Obviously, in either approach, a discrepancy between the indicated optimal complexity (indicated as the minimum of a generalization error estimate or a bound) and the genuine minimum of unknown true risks is possible. In the paper, this problem is posed in a novel quantitative way. We state and prove theorems demonstrating how one can calculate pessimistic probabilities of discrepancy between these minima for given for given conditions of an experiment. The probabilities are calculated in terms of all relevant constants: the sample size, the number of cross-validation folds, the capacity of the set of approximating functions and bounds on this set. We report experiments carried out to validate the results.

Słowa kluczowe

EN

regression estimation; model comparison; complexity selection; cross-validation; generalization; statistical learning theory; generalization bounds; structural risk minimization

• Wydawca: University of Zielona Gora Press
• Czasopismo: International Journal of Applied Mathematics and Computer Science
• Rocznik: 2010
• Tom: 20
• Numer: 3
• Strony: 525-544

Daty

wydano

2010

otrzymano

2009-06-19

poprawiono

2010-03-16

poprawiono

2010-03-19

Twórcy

autor

Przemysław Klęsk

• Department of Methods of Artificial Intelligence and Applied Mathematics, Westpomeranian University of Technology, ul. Żołnierska 49, 71-210 Szczecin, Poland

Bibliografia

• Anthony, M. and Shawe-Taylor, J. (1993). A result of Vapnik with applications, Discrete Applied Mathematics 48(3): 207-217.
• Bartlett, P. (1998). The sample complexity of pattern classification with neural networks: The size of the weights is more important than the size of the network, IEEE Transactions on Information Theory 44(2): 525-536.
• Bartlett, P., Kulkarni, S. and Posner, S. (1997). Covering numbers for real-valued function classes, IEEE Transactions on Information Theory 43(5): 1721-1724.
• Bartlett, P. and Tewari, A. (2007). Sample complexity of policy search with known dynamics, Advances in Neural Information Processing Systems 19: 97-104.
• Berry, A. (1941). The accuracy of the Gaussian approximation to the sum of independent variates, Transactions of the American Mathematical Society 49(1): 122-136.
• Bousquet, L., Boucheron S. and Lugosi G. (2004). Introduction to Statistical Learning Theory, Advanced Lectures in Machine Learning, Springer, Heidelberg, pp. 169-207.
• Cherkassky, V. and Mulier, F. (1998). Learning from Data, John Wiley & Sons, Hoboken, NJ.
• DasGupta, A. (2008). Asymptotic Theory of Statistics and Probability, Springer, New York, NY.
• Devroye, L., Gyorfi, L. and Lugosi, G. (1996). A Probabilistic Theory of Pattern Recognition, Springer-Verlag, New York, NY.
• Efron, B. and Tibshirani, R. (1993). An Introduction to Bootstrap, Chapman & Hall, London.
• Esséen, C. (1942). On the Liapounoff limit of error in the theory of probability, Arkiv fdr Matematik, Astronomi och Fysik 28A(9): 1-19.
• Esséen, C. (1956). A moment inequality with an application to the central limit theorem, Skand. Aktuarietidskr. 39: 160-170.
• Fu, W., Caroll, R. and Wang, S. (2005). Estimating misclassification error with small samples via bootstrap crossvalidation, Bioinformatics 21(9): 1979-1986.
• Graham, R., Knuth, D. and Patashnik, O. (2002). Matematyka konkretna (Concrete Mathematics. A Foundation for Computer Science), PWN, Warsaw.
• Hasterberg, T., Choi, N. H., Meier, L. and Fraley C. (2008). Least angle and l1 penalized regression: A review, Statistics Surveys 2: 61-93.
• Hellman, M. and Raviv, J. (1970). Probability of error, equivocation and the Chernoff bound, IEEE Transactions on Information Theory 16(4): 368-372.
• Hjorth, J. (1994). Computer Intensive Statistical Methods Validation, Model Selection, and Bootstrap, Chapman & Hall, London.
• Knuth, D. (1997). The Art of Computer Programming, AddisonWesley, Reading, MA.
• Kohavi, R. (1995). A study of cross-validation and boostrap for accuracy estimation and model selection, International Joint Conference on Artificial Intelligence (IJCAI), Montreal, Quebec, Canada, pp. 1137-1143.
• Korzeń, M. and Klęsk, P. (2008). Maximal margin estimation with perceptron-like algorithm, in L. Rutkowski, R. Sche˙ rer, R. Tadeusiewicz, L.A. Zadeh and J. Zurada (Eds.), Artificial Intelligence and Soft Computing-ICAISC 2008, Lecture Notes in Artificial Intelligence, Vol. 5097, Springer, Berlin, Heidelberg, pp. 597-608.
• Krzyżak, A., Kohler M., and Schäfer D. (2000). Application of structural risk minimization to multivariate smoothing spline regression estimates, Bernoulli 8(4): 475-489.
• Ng, A. (2004). Feature selection, l₁ vs. l₂ regularization, and rotational invariance, ACM International Conference on Machine Learning, Banff, Alberta, Canada, Vol. 69, pp. 78-85.
• Schmidt, J., Siegel, A. and Srinivasan, A. (1995). ChernoffHoeffding bounds for applications with limited independence, SIAM Journal on Discrete Mathematics 8(2): 223-250.
• Shawe-Taylor, J., Bartlett, P., Williamson, R. and Anthony, M. (1996). A framework for structural risk minimization, COLT, ACM Press, New York, NY, pp. 68-76.
• Shevtsova, I. (2007). Sharpening of the upper bound of the absolute constant in the Berry-Esséen inequality, Theory of Probability and its Applications 51(3): 549-553.
• van Beek, P. (1972). An application of Fourier methods to the problem of sharpening the Berry-Esséen inequality, Zeitschrift für Wahrscheinlichkeitstheorie und verwandte Gebiete 23: 187-196.
• Vapnik, V. (1995). The Nature of Statistical Learning Theory, Springer, New York, NY.
• Vapnik, V. (1998). Statistical Learning Theory: Inference from Small Samples, Wiley, New York, NY.
• Vapnik, V. (2006). Estimation of Dependencies Based on Empirical Data, Information Science & Statistics, Springer, New York, NY.
• Vapnik, V. and Chervonenkis, A. (1968). On the uniform convergence of relative frequencies of events to their probabilities, Doklady Akademii Nauk 9(4): 915-918.
• Vapnik, V. and Chervonenkis, A. (1989). The necessary and sufficient conditions for the consistency of the method of empirical risk minimization, Yearbook of the Academy of Sciences of the USSR on Recognition, Classification and Forecasting, Vol. 2, pp. 217-249.
• Weiss, S. and Kulikowski, C. (1991). Computer Systems That Learn, Morgan Kauffman Publishers, San Francisco, CA.
• Zhang, T. (2002). Covering number bounds of certain regularized linear function classes, Journal of Machine Learning Research 2: 527-550.

Identyfikatory

DOI

10.2478/v10006-010-0039-x