A RESULT OF VAPNIK WITH APPLICATIONS

被引：36

作者：

ANTHONY, M ^{[1
]}

SHAWETAYLOR, J ^{[1
]}

机构：

[1] UNIV LONDON ROYAL HOLLOWAY & BEDFORD NEW COLL,DEPT COMP SCI,EGHAM TW20 0EX,SURREY,ENGLAND

来源：

DISCRETE APPLIED MATHEMATICS | 1993年 / 47卷 / 03期

关键词：

D O I：

10.1016/0166-218X(93)90126-9

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

A new proof of a result due to Vapnik is given. Its implications for the theory of PAC learnability are discussed, with particular reference to the learnability of functions taking values in a countable set. An application to the theory of artificial neural networks is then given.

引用

页码：207 / 217

页数：11

共 16 条

[1] ANTHONY M, 1990, COLT 90, P246
[2] What Size Net Gives Valid Generalization?
Baum, Eric B.
Haussler, David
[J]. NEURAL COMPUTATION, 1989, 1 (01) : 151 - 160
[3] BIGGS N, IN PRESS DISCRETE MA
[4] LEARNABILITY AND THE VAPNIK-CHERVONENKIS DIMENSION
BLUMER, A
EHRENFEUCHT, A
HAUSSLER, D
WARMUTH, MK
[J]. JOURNAL OF THE ACM, 1989, 36 (04) : 929 - 965
[5] DECISION THEORETIC GENERALIZATIONS OF THE PAC MODEL FOR NEURAL NET AND OTHER LEARNING APPLICATIONS
HAUSSLER, D
[J]. INFORMATION AND COMPUTATION, 1992, 100 (01) : 78 - 150
[6] QUANTIFYING INDUCTIVE BIAS - AI LEARNING ALGORITHMS AND VALIANTS LEARNING FRAMEWORK
HAUSSLER, D
[J]. ARTIFICIAL INTELLIGENCE, 1988, 36 (02) : 177 - 221
[7] HAUSSLER D, 1989, UCSCCRL8930 U CAL CO
[8] McClelland J. L., 1986, PARALLEL DISTRIBUTED, V1
[9] Natarajan B. K., 1989, Machine Learning, V4, P67, DOI 10.1023/A:1022605311895
[10] Pollard D., 1984, CONVERGENCE STOCHAST

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