| 000 | 00000cam u2200205 a 4500 | |
| 001 | 000046037183 | |
| 005 | 20200715094116 | |
| 008 | 200714s1999 enka b 001 0 eng d | |
| 010 | ▼a 98053260 | |
| 020 | ▼a 052157353X (hardback) | |
| 020 | ▼a 9780521118620 (pbk.) | |
| 035 | ▼a (KERIS)REF000006685282 | |
| 040 | ▼a DLC ▼c DLC ▼d DLC ▼d 211009 | |
| 050 | 0 0 | ▼a QA76.87 ▼b .A58 1999 |
| 082 | 0 0 | ▼a 006.3/2 ▼2 23 |
| 084 | ▼a 006.32 ▼2 DDCK | |
| 090 | ▼a 006.32 ▼b A628n | |
| 100 | 1 | ▼a Anthony, Martin. |
| 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 |
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.