| 000 | 01075camuu2200313ia 4500 | |
| 001 | 000045406019 | |
| 005 | 20071207113151 | |
| 008 | 920727s1997 enka b 001 0 eng d | |
| 020 | ▼a 0521599229 | |
| 020 | ▼a 9780521599221 | |
| 029 | 1 | ▼a YDXCP ▼b 1334958 |
| 035 | ▼a (OCoLC)37241615 | |
| 040 | ▼a MNU ▼c MNU ▼d IQU ▼d BAKER ▼d YDXCP ▼d KUB ▼d 211009 | |
| 049 | ▼a KUBA | |
| 050 | 4 | ▼a Q325.5 ▼b .A58 1997 |
| 082 | 0 4 | ▼a 006.31 ▼2 22 |
| 090 | ▼a 006.31 ▼b A628c | |
| 100 | 1 | ▼a Anthony, Martin. |
| 245 | 1 0 | ▼a Computational learning theory : ▼b an introdution / ▼c Martin Anthony & Norman Biggs. |
| 250 | ▼a 1st paperback ed. (with corrections) | |
| 260 | ▼a Cambridge, U.K. ; ▼a New York, NY : ▼b Cambridge University Press , ▼c 1997. | |
| 300 | ▼a 157 p. : ▼b ill. ; ▼c 25 cm. | |
| 440 | 0 | ▼a Cambridge tracts in theoretical computer science ; ▼v 30 |
| 504 | ▼a Includes bibliographical references (p. [143]-149) and index. | |
| 650 | 0 | ▼a Machine learning. |
| 700 | 1 | ▼a Biggs, Norman. |
| 945 | ▼a KINS | |
| 994 | ▼a C0 ▼b KUB |
소장정보
| No. | 소장처 | 청구기호 | 등록번호 | 도서상태 | 반납예정일 | 예약 | 서비스 |
|---|---|---|---|---|---|---|---|
| No. 1 | 소장처 과학도서관/Sci-Info(2층서고)/ | 청구기호 006.31 A628c | 등록번호 121161813 | 도서상태 대출가능 | 반납예정일 | 예약 | 서비스 |
컨텐츠정보
책소개
Computational learning theory is one of the first attempts to construct a mathematical theory of a cognitive process. It has been a field of much interest and rapid growth in recent years. This text provides a framework for studying a variety of algorithmic processes, such as those currently in use for training artificial neural networks. The authors concentrate on an approximate model for learning and gradually develop the ideas of efficiency considerations. Finally, they consider applications of the theory to artificial neural networks. An abundance of exercises and an extensive list of references round out the text. This volume provides a comprehensive review of the topic, including information drawn from logic, probability, and complexity theory. It forms a solid introduction to the theory of comptutational learning suitable for a broad spectrum of graduate students from theoretical computer science to mathematics.
정보제공 :
목차
1. Concepts, hypotheses, learning algorithms; 2. Boolean formulae and representations; 3. Probabilistic learning; 4. Consistent algorithms and learnability; 5. Efficient learning I; 6. Efficient learning II; 7. The VC dimension; 8. Learning and the VC dimension; 9. VC dimension and efficient learning; 10. Linear threshold networks.
정보제공 :