Combinatorial Variability of Vapnik-chervonenkis Classes with Applications to Sample Compression Schemes

[1]  Peter L. Bartlett,et al.  Function Learning from Interpolation , 1995, Combinatorics, Probability and Computing.

[2]  G. Lugosi,et al.  Adaptive Model Selection Using Empirical Complexities , 1998 .

[3]  Peter L. Bartlett,et al.  The Sample Complexity of Pattern Classification with Neural Networks: The Size of the Weights is More Important than the Size of the Network , 1998, IEEE Trans. Inf. Theory.

[4]  Sanjeev R. Kulkarni,et al.  Covering numbers for real-valued function classes , 1997, IEEE Trans. Inf. Theory.

[5]  Shai Ben-David,et al.  A composition theorem for learning algorithms with applications to geometric concept classes , 1997, STOC '97.

[6]  Peter L. Bartlett,et al.  Efficient agnostic learning of neural networks with bounded fan-in , 1996, IEEE Trans. Inf. Theory.

[7]  P. R. Kumar,et al.  Learning by canonical smooth estimation. I. Simultaneous estimation , 1996, IEEE Trans. Autom. Control..

[8]  P. R. Kumar,et al.  Learning by canonical smooth estimation. II. Learning and choice of model complexity , 1996, IEEE Trans. Autom. Control..

[9]  Gábor Lugosi,et al.  A data-dependent skeleton estimate for learning , 1996, COLT '96.

[10]  László Györfi,et al.  A Probabilistic Theory of Pattern Recognition , 1996, Stochastic Modelling and Applied Probability.

[11]  John Shawe-Taylor,et al.  A framework for structural risk minimisation , 1996, COLT '96.

[12]  Philip M. Long,et al.  More theorems about scale-sensitive dimensions and learning , 1995, COLT '95.

[13]  Luc Devroye,et al.  Lower bounds in pattern recognition and learning , 1995, Pattern Recognit..

[14]  Philip M. Long,et al.  Fat-shattering and the learnability of real-valued functions , 1994, COLT '94.

[15]  Sanjeev R. Kulkarni,et al.  A metric entropy bound is not sufficient for learnability , 1994, IEEE Trans. Inf. Theory.

[16]  John Shawe-Taylor,et al.  A Result of Vapnik with Applications , 1993, Discret. Appl. Math..

[17]  Noga Alon,et al.  Scale-sensitive dimensions, uniform convergence, and learnability , 1993, Proceedings of 1993 IEEE 34th Annual Foundations of Computer Science.

[18]  Paul W. Goldberg,et al.  Bounding the Vapnik-Chervonenkis Dimension of Concept Classes Parameterized by Real Numbers , 1993, COLT '93.

[19]  David Haussler,et al.  Decision Theoretic Generalizations of the PAC Model for Neural Net and Other Learning Applications , 1992, Inf. Comput..

[20]  Michael C. Laskowski,et al.  Vapnik-Chervonenkis classes of definable sets , 1992 .

[21]  Sanjeev R. Kulkarni,et al.  Problems of computational and informational complexity in machine vision and learning , 1991 .

[22]  Leonard Pitt,et al.  Prediction-Preserving Reducibility , 1990, J. Comput. Syst. Sci..

[23]  Robert E. Schapire,et al.  Efficient distribution-free learning of probabilistic concepts , 1990, Proceedings [1990] 31st Annual Symposium on Foundations of Computer Science.

[24]  N. Littlestone.  Mistake bounds and logarithmic linear-threshold learning algorithms , 1990 .

[25]  Sally Floyd,et al.  Space-bounded learning and the Vapnik-Chervonenkis dimension , 1989, COLT '89.

[26]  David Haussler,et al.  Learnability and the Vapnik-Chervonenkis dimension , 1989, JACM.

[27]  Alon Itai,et al.  Learnability by fixed distributions , 1988, COLT '88.

[28]  Vojtech Rödl,et al.  Geometrical realization of set systems and probabilistic communication complexity , 1985, 26th Annual Symposium on Foundations of Computer Science (sfcs 1985).

[29]  D. Pollard.  Convergence of stochastic processes , 1984 .

[30]  V. Vapnik,et al.  Necessary and Sufficient Conditions for the Uniform Convergence of Means to their Expectations , 1982 .

[31]  Norbert Sauer,et al.  On the Density of Families of Sets , 1972, J. Comb. Theory, Ser. A.

[32]  Vladimir Vapnik,et al.  Chervonenkis: On the uniform convergence of relative frequencies of events to their probabilities , 1971 .

[33]  Chen C. Chang,et al.  Model Theory: Third Edition (Dover Books On Mathematics) By C.C. Chang;H. Jerome Keisler;Mathematics , 1966 .

[34]  A E Bostwick,et al.  THE THEORY OF PROBABILITIES. , 1896, Science.