| 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 |
Holdings Information
| No. | Location | Call Number | Accession No. | Availability | Due Date | Make a Reservation | Service |
|---|---|---|---|---|---|---|---|
| No. 1 | Location Science & Engineering Library/Sci-Info(Stacks2)/ | Call Number 006.31 A628c | Accession No. 121161813 | Availability Available | Due Date | Make a Reservation | Service |
Contents information
Book Introduction
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.
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Table of Contents
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.
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