On the Learnability of Rich Function Classes
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[1] M. Birman,et al. PIECEWISE-POLYNOMIAL APPROXIMATIONS OF FUNCTIONS OF THE CLASSES $ W_{p}^{\alpha}$ , 1967 .
[2] Russell Greiner,et al. Computational learning theory and natural learning systems , 1997 .
[3] David Haussler,et al. Sphere Packing Numbers for Subsets of the Boolean n-Cube with Bounded Vapnik-Chervonenkis Dimension , 1995, J. Comb. Theory, Ser. A.
[4] D. Pollard. Convergence of stochastic processes , 1984 .
[5] Brian D. Ripley,et al. Pattern Recognition and Neural Networks , 1996 .
[6] A. Barron. Approximation and Estimation Bounds for Artificial Neural Networks , 1991, COLT '91.
[7] Henryk Wozniakowski,et al. Information-based complexity , 1987, Nature.
[8] Y. Abu-Mostafa. Machines that Learn from Hints , 1995 .
[9] A. Pinkus. n-Widths in Approximation Theory , 1985 .
[10] H. Woxniakowski. Information-Based Complexity , 1988 .
[11] Jude W. Shavlik,et al. Interpretation of Artificial Neural Networks: Mapping Knowledge-Based Neural Networks into Rules , 1991, NIPS.
[12] Volker Tresp,et al. Incorporating prior knowledge into networks of locally-tuned units , 1994, COLT 1994.
[13] G. Lugosi,et al. Adaptive Model Selection Using Empirical Complexities , 1998 .
[14] Yaser S. Abu-Mostafa,et al. Hints and the VC Dimension , 1993, Neural Computation.
[15] Joel Ratsaby,et al. The Degree of Approximation of Sets in Euclidean Space Using Sets with Bounded Vapnik-Chervonenkis Dimension , 1998, Discret. Appl. Math..
[16] Noga Alon,et al. Scale-sensitive dimensions, uniform convergence, and learnability , 1993, Proceedings of 1993 IEEE 34th Annual Foundations of Computer Science.
[17] John Shawe-Taylor,et al. A framework for structural risk minimisation , 1996, COLT '96.
[18] Yaser S. Abu-Mostafa,et al. Learning from hints in neural networks , 1990, J. Complex..
[19] Keinosuke Fukunaga,et al. Introduction to Statistical Pattern Recognition , 1972 .
[20] Yoshua Bengio,et al. Pattern Recognition and Neural Networks , 1995 .
[21] G. Lorentz,et al. Constructive approximation : advanced problems , 1996 .
[22] Richard O. Duda,et al. Pattern classification and scene analysis , 1974, A Wiley-Interscience publication.
[23] Noga Alon,et al. Scale-sensitive dimensions, uniform convergence, and learnability , 1997, JACM.
[24] Leslie G. Valiant,et al. A theory of the learnable , 1984, STOC '84.
[25] Halbert White,et al. Estimation, inference, and specification analysis , 1996 .
[26] Joel Ratsaby,et al. Generalization of the PAC-Model for Learning with Partial Information , 1997, EuroCOLT.
[27] David Haussler,et al. Learnability and the Vapnik-Chervonenkis dimension , 1989, JACM.
[28] D. Pollard. Empirical Processes: Theory and Applications , 1990 .
[29] Robert A. Jacobs,et al. Methods For Combining Experts' Probability Assessments , 1995, Neural Computation.
[30] László Györfi,et al. A Probabilistic Theory of Pattern Recognition , 1996, Stochastic Modelling and Applied Probability.
[31] Gábor Lugosi,et al. Concept learning using complexity regularization , 1995, IEEE Trans. Inf. Theory.
[32] David Haussler,et al. Decision Theoretic Generalizations of the PAC Model for Neural Net and Other Learning Applications , 1992, Inf. Comput..
[33] Ron Meir,et al. Towards robust model selection using estimation and approximation error bounds , 1996, COLT '96.
[34] Joel Ratsaby,et al. On the Degree of Approximation by Manifolds of Finite Pseudo-Dimension , 1999 .