Vapnik-Chervonenkis dimension of neural nets

[1]  Peter L. Bartlett,et al.  Neural Network Learning - Theoretical Foundations , 1999 .

[2]  Peter L. Bartlett,et al.  Almost Linear VC-Dimension Bounds for Piecewise Polynomial Networks , 1998, Neural Computation.

[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]  Eduardo Sontag.  VC dimension of neural networks , 1998 .

[5]  Michael Schmitt,et al.  On the Complexity of Learning for Spiking Neurons with Temporal Coding , 1999, Inf. Comput..

[6]  Marek Karpinski,et al.  Polynomial Bounds for VC Dimension of Sigmoidal and General Pfaffian Neural Networks , 1997, J. Comput. Syst. Sci..

[7]  Eduardo D. Sontag,et al.  Neural Networks with Quadratic VC Dimension , 1995, J. Comput. Syst. Sci..

[8]  Wolfgang Maass,et al.  Bounds for the computational power and learning complexity of analog neural nets , 1993, SIAM J. Comput..

[9]  C. SIAMJ..  BOUNDS FOR THE COMPUTATIONAL POWER AND LEARNING COMPLEXITY OF ANALOG NEURAL NETS , 1997 .

[10]  Pascal Koiran.  VC dimension in circuit complexity , 1996, Proceedings of Computational Complexity (Formerly Structure in Complexity Theory).

[11]  Peter L. Bartlett,et al.  The VC Dimension and Pseudodimension of Two-Layer Neural Networks with Discrete Inputs , 1996, Neural Computation.

[12]  Marek Karpinski,et al.  Polynomial bounds for VC dimension of sigmoidal neural networks , 1995, STOC '95.

[13]  Wolfgang Maass,et al.  Agnostic PAC Learning of Functions on Analog Neural Nets , 1993, Neural Computation.

[14]  K. Siu,et al.  Theoretical Advances in Neural Computation and Learning , 1994, Springer US.

[15]  Wolfgang Maass,et al.  Neural Nets with Superlinear VC-Dimension , 1994, Neural Computation.

[16]  Wolfgang Maass,et al.  On the Computational Complexity of Networks of Spiking Neurons , 1994, NIPS.

[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]  Eduardo D. Sontag,et al.  Finiteness results for sigmoidal “neural” networks , 1993, STOC.

[20]  A. Sakurai,et al.  Tighter bounds of the VC-dimension of three layer networks , 1993 .

[21]  Eduardo D. Sontag,et al.  Feedforward Nets for Interpolation and Classification , 1992, J. Comput. Syst. Sci..

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

[23]  David Haussler,et al.  What Size Net Gives Valid Generalization? , 1989, Neural Computation.

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