A Fixed-Distribution PAC Learning Theory for Neural FIR Models-Reference-Cited by-同舟云学术

A Fixed-Distribution PAC Learning Theory for Neural FIR Models

Published:2005-11 Issue:3 Volume:25 Page:275-291
ISSN:0925-9902
Container-title:Journal of Intelligent Information Systems
language:en
Short-container-title:J Intell Inf Syst

Author:

Najarian Kayvan

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Computer Networks and Communications,Hardware and Architecture,Information Systems,Software

Link

http://link.springer.com/content/pdf/10.1007/s10844-005-0194-y.pdf

Reference35 articles.

1. Bartlett, P.(1996). The Sample Complexity of Pattern Classification with Neural Networks: The Size of the Weights is More Important Than the Size of the Network. Amer. Statistical Assoc. Math. Soc. Transactions, 17, 277–364.

2. Barron, A.R. (1994). Approximation and Estimation Bounds for Artificial Neural Networks. Machine Learning, 14, 115–133.

3. Barron, A.R. (1994). Approximation and Estimation Bounds for Artificial Neural Networks. Machine Learning, 14, 115–133.

4. Benedek, G.M. and Itai, A. (1991a). Dominating Distributions and Learnability. In Proc. Fifth Workshop on Computational Learning Theory, ACM (pp. 253–264).

5. Benedek, G.M. and Itai, A. (1991a). Dominating Distributions and Learnability. In Proc. Fifth Workshop on Computational Learning Theory, ACM (pp. 253–264).

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