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Neural network learning : theoretical foundations

Neural network learning : theoretical foundations (1회 대출)

자료유형
단행본
개인저자
Anthony, Martin. Bartlett, Peter L., 1966-.
서명 / 저자사항
Neural network learning : theoretical foundations / Martin Anthony and Peter L. Bartlett.
발행사항
Cambridge ;   New York, NY :   Cambridge University Press,   1999   (2009printing).  
형태사항
xiv, 389 p. : ill. ; 23 cm.
ISBN
052157353X (hardback) 9780521118620 (pbk.)
서지주기
Includes bibliographical references (p. 365-378) and indexes.
일반주제명
Neural networks (Computer science).
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245 1 0 ▼a Neural network learning : ▼b theoretical foundations / ▼c Martin Anthony and Peter L. Bartlett.
260 ▼a Cambridge ; ▼a New York, NY : ▼b Cambridge University Press, ▼c 1999 ▼g (2009printing).
300 ▼a xiv, 389 p. : ▼b ill. ; ▼c 23 cm.
504 ▼a Includes bibliographical references (p. 365-378) and indexes.
650 0 ▼a Neural networks (Computer science).
700 1 ▼a Bartlett, Peter L., ▼d 1966-.
945 ▼a KLPA

소장정보

No. 소장처 청구기호 등록번호 도서상태 반납예정일 예약 서비스
No. 1 소장처 중앙도서관/서고6층/ 청구기호 006.32 A628n 등록번호 111830851 도서상태 대출가능 반납예정일 예약 서비스 B M

컨텐츠정보

목차

1. Introduction; Part I. Pattern Recognition with Binary-output Neural Networks: 2. The pattern recognition problem; 3. The growth function and VC-dimension; 4. General upper bounds on sample complexity; 5. General lower bounds; 6. The VC-dimension of linear threshold networks; 7. Bounding the VC-dimension using geometric techniques; 8. VC-dimension bounds for neural networks; Part II. Pattern Recognition with Real-output Neural Networks: 9. Classification with real values; 10. Covering numbers and uniform convergence; 11. The pseudo-dimension and fat-shattering dimension; 12. Bounding covering numbers with dimensions; 13. The sample complexity of classification learning; 14. The dimensions of neural networks; 15. Model selection; Part III. Learning Real-Valued Functions: 16. Learning classes of real functions; 17. Uniform convergence results for real function classes; 18. Bounding covering numbers; 19. The sample complexity of learning function classes; 20. Convex classes; 21. Other learning problems; Part IV. Algorithmics: 22. Efficient learning; 23. Learning as optimisation; 24. The Boolean perceptron; 25. Hardness results for feed-forward networks; 26. Constructive learning algorithms for two-layered networks.

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