[1]    Peter Bartlett, “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, Vol. 44, No. 2, March 1998.

[2]    Gyora M. Benedek and Alon Itai, “Learnability by Fixed Distributions”, Proceedings of the 1988 Workshop on Computational Learning Theory.

[3]    Avrim Blum, Adam Kalai, and John Langford 1999. Beating the Holdout: Bounds for KFold and Progressive Cross-Validation. COLT99. http://www.cs.cmu.edu/~jcl/papers/progressive_validation/coltfinal.ps

[4]    Avrim Blum and Ron Rivest, “Training a 3-Node Neural Network is NP-Complete”, Neural Networks, 5(1):117-127, 1992.

[5]    A. Blumer, A. Ehrenfeucht, D. Haussler, M. Warmuth. “Occam’s Razor.” Information Processing Letters 24: 377-380, 1987.

[6]    L. Breiman, "Bagging Predictors" Machine Learning, Vol. 24, No . 2, pp. 123-140.

[7]    H. Chernoff, “A Measure of the asymptotic efficiency of tests of a hypothesis based on the sum of observations”, Annals of Mathematical Statistics, 23:493-507, 1952

[8]    N. Christianini, J. Shaw-Taylor, “Support Vector Machines”, Cambridge University Press, 2000 ISBN: 0 521 78019 5

[9]    Claude Cohen-Tannoudji, Bernard Diu, Frank Laloe, Bernard Dui, “Quantum Mechanics”

[10]    Thomas Cover and Joy Thomas, “Elements of Information Theory” Wiley, New York 1991.

[11]    Luc Devroye, Laszlo Gyorfi, Gabor Lugosi, “A Probabilistic Theory of Pattern Recognition” Springer-Verlag New York, 1996.

[12]    Pedro Domingos. “A Process-Oriented Heuristic for Model Selection, Machine Learning Proceedings of the Fifteenth International Conference, "Morgan Kaufmann Publishers, San Francisco, CA, 1998, pp 127-135.

[13]    R. M. Dudley, “A course on empirical processes”, Lecture Notes in Mathematics 1097, 2-141. Springer-Verlag, New York.

[14]    A. Ehrenfucht, D. Haussler, M. Kearns, and L. Valiant, “A General Lower Bound on the Number of Examples Needed for Learning”, Information and Computation 82(3), pp. 247-261, 1989.

[15]    B. Efron and R. Tibshirani, “An Introduction to the Bootstrap”, Chapman & Hall, London, 1993.

[16]    Yoav Freund. “Predicting a binary sequence almost as well as the optimal biased coin”, COLT 1996. http://citeseer.nj.nec.com/freund96predicting.html

[17]    Yoav Freund. Self-Bounding Learning Algorithms. COLT 98. http://www.research.att.com/~yoav/papers/lsearch.ps.gz

[18]    Yoav Freund and Robert E. Schapire, “A Decision Theoretic Generalization of On-line Learning and an Application to Boosting” Eurocolt 1995

[19]    Oded Goldreich, “The Foundations of Cryptography - Volume 1”, ISBN 0-521-79172-3 Cambridge University Press, 2001

[20]    David Haussler, “Mathematical perspectives on Neural networks ”, Lawrence Erlbaum Associates, 1995.

[21]    David Haussler, Michael Kearns, and Robert Schapire, “Bounds on the Sample Complexity of Bayesian Learning Using Information Theory and the VC dimension”, Machine Learning 14, pp. 83-113, 1994.

[22]    David Haussler, Michael Kearns, H. Sebastian Seung, and Naftali Tishby, “Rigorous Learning Curve Bounds from Statistical Mechanics” Machine Learning,25, 1996, pages 195–236.

[23]    Hoeffding, W. (1963). Probability inequalities for sums of bounded random variables. Journal of the American Statistical Association, 58, 13-30

[24]    T. Jaakkola, M. Meila, T. Jebara, “Maximum Entropy D iscrimination\char” NIPS 1999.

[25]    Adam Kalai, “Probabilistic and On-line methods in Machine Learning”.thesis, CMU-CS-01-132.

[26]    Michael Kearns. Efficient Noise-Tolerant Learning From Statistical Queries, Proceedings of the 25th ACM Symposium on the Theory of Computing, pp. 392-401, 1993, ACM Press. http://www.research.att.com/~mkearns/papers/sq-noise.ps.Z

[27]    Michael Kearns and Dana Ron, “Algorithmic Stability and Sanity-Check Bounds for Leave-One-Out Cross-Validation.” Neural Computation 11(6), pages 1427-1453, 1999. Also in Proceedings of the Tenth Annual Conference on Computational Learning Theory, ACM Press, 1997, pages 152–162.

[28]    V. Koltchinskii and D. Panchenko, “Empirical Margin Distributions and Bounding the Generalization Error of Combined Classifiers”, preprint, http://citeseer.nj.nec.com/386416.html

[29]    P.Kontkanen, P. Myllymaki, T. Silander, H.Tirri, and P. Grunwald, Predictive Distributions and Bayesian Networks, Journal of Statistics and Computing 10, pp. 39-54, 2000.

[30]    John Langford and Avrim Blum 1999. Microchoice Bounds and Self Bounding learning algorithms. COLT99. http://www.cs.cmu.edu/~jcl/papers/microchoice/mc.ps

[31]    John Langford and Avrim Blum 1999. Microchoice Bounds and Self Bounding learning algorithms. Machine Learning Journal. http://www.cs.cmu.edu/~jcl/papers/microchoice/journal/journal_final.ps

[32]    John Langford and Rich Caruana, (Not) Bounding the True Error NIPS2001.

[33]    John Langford and David McAllester, “Computable Shell Decomposition Bounds” COLT 2000.

[34]    John Langford, Matthias Seeger, and Nimrod Megiddo, “An Improved Predictive Accuracy Bound for Averaging Classifiers” ICML2001.

[35]    John Langford and Matthias Seeger, “Bounds for Averaging Classifiers.” CMU tech report, CMU-CS-01-102, 2001.

[36]    N. Littlestone. “From on-line to batch learning” In Proceedings of the 2nd Annual Workshop on Computational Learning Theory, pp. 269–284, 1989.

[37]    Tom Mitchell, “Machine Learning”, McGraw Hill, 1997.

[38]    Yishay Mansour, “Pessimistic Decision Tree Pruning Based on Tree Size”, ICML1997.

[39]    David McAllester, “PAC-Bayesian Model Averaging” COLT 1999.

[40]    Colin McDiarmid, “On the method of bounded differences”, In \emph {Surveys in Combinatorics 1989,} pages 148-188. Cambridge University Press, 1989.

[41]    Jon Mingers, An Empirical Comparison of Pruning Methods for Decision Tree Induction, Machine Learning, 1989, vol. 4, 227-243.

[42]    Dimitry Panchenko, personal communications, 2001

[43]    David Pollard, “Convergence of Stochastic Processes”, Springe r-Verlag 1984.

[44]    J.L. Quinlan, Induction of Decision Trees, Machine Learning, 1986, vol. 1, pp 81-106.

[45]    R.L. Rivest, Learning Decision Lists. Machine Learning, 1987, vol. 2, pp 229-246.

[46]    Robert E. Schapire, Yoav Freund, Peter Bartlett, and Wee Sun Lee, “Boosting the Margin: A new explanation for the effectiveness of voting methods” The Annals of Statistics, 26(5):1651-1686, 1998.

[47]    Tobias Scheffer and Thorsten Joachims, “Expected Error Analysis for Model Selection”, ICML 1999.

[48]    Matthias Seeger, “PAC-Bayesian Generalization Error Bounds for Gaussian Processes”, Tech Report, Division of Informatics report EDI-INF-RR-0094. http://www.dai.ed.ac.uk/homes/seeger/papers/gpmcall-tr.ps.gz

[49]    Aad W. van der Vaart and Jon A. Wellner, “Weak Convergence and Empirical Processes”, Springer-Verlag 1996.

[50]    L.G. Valiant. “A Theory of the Learnable.” Communications of the ACM 27(11):1134-1142, November 1984

[51]    V. N. Vapnik and A. Y. Chervonenkis. “On the uniform convergence of relative frequencies of events to their probabilities.” Theory of Probab. and its Applications, 16(2):264-280, 1971.

[52]    Vladimir N. Vapnik, “Statistical Learning Theory”, Wiley, December 1999.

[53]    Jon Baxter. A Model of Inductive Bias Learning. 1998. http://wwwsyseng.anu.edu.au/~jon/papers/ltolchap.ps.gz