An introduction to computational learning theory / (Record no. 73059)
[ view plain ]
| 000 -LEADER | |
|---|---|
| fixed length control field | 03884nam a2200529 i 4500 |
| 001 - CONTROL NUMBER | |
| control field | 6267405 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20220712204655.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 151223s1994 maua ob 001 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| -- | |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| ISBN | 9780262276863 |
| -- | ebook |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| -- | electronic |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| -- | electronic |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| -- | electronic |
| 100 1# - AUTHOR NAME | |
| Author | Kearns, Michael J., |
| 245 13 - TITLE STATEMENT | |
| Title | An introduction to computational learning theory / |
| 300 ## - PHYSICAL DESCRIPTION | |
| Number of Pages | 1 PDF (xii, 207 pages) : |
| 505 0# - FORMATTED CONTENTS NOTE | |
| Remark 2 | The probably approximately correct learning model -- Occam's razor -- The Vapnik-Chervonenkis dimension -- Weak and strong learning -- Learning in the presence of noise -- Inherent unpredictability -- Reducibility in PAC learning -- Learning finite automata by experimentation -- Appendix: some tools for probabilistic analysis. |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc | Emphasizing issues of computational efficiency, Michael Kearns and Umesh Vazirani introduce a number of central topics in computational learning theory for researchers and students in artificial intelligence, neural networks, theoretical computer science, and statistics.Computational learning theory is a new and rapidly expanding area of research that examines formal models of induction with the goals of discovering the common methods underlying efficient learning algorithms and identifying the computational impediments to learning.Each topic in the book has been chosen to elucidate a general principle, which is explored in a precise formal setting. Intuition has been emphasized in the presentation to make the material accessible to the nontheoretician while still providing precise arguments for the specialist. This balance is the result of new proofs of established theorems, and new presentations of the standard proofs.The topics covered include the motivation, definitions, and fundamental results, both positive and negative, for the widely studied L. G. Valiant model of Probably Approximately Correct Learning; Occam's Razor, which formalizes a relationship between learning and data compression; the Vapnik-Chervonenkis dimension; the equivalence of weak and strong learning; efficient learning in the presence of noise by the method of statistical queries; relationships between learning and cryptography, and the resulting computational limitations on efficient learning; reducibility between learning problems; and algorithms for learning finite automata from active experimentation. |
| 700 1# - AUTHOR 2 | |
| Author 2 | Vazirani, Umesh Virkumar. |
| 856 42 - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | https://ieeexplore.ieee.org/xpl/bkabstractplus.jsp?bkn=6267405 |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | eBooks |
| 264 #1 - | |
| -- | Cambridge, Massachusetts : |
| -- | MIT Press, |
| -- | c1994. |
| 264 #2 - | |
| -- | [Piscataqay, New Jersey] : |
| -- | IEEE Xplore, |
| -- | [1994] |
| 336 ## - | |
| -- | text |
| -- | rdacontent |
| 337 ## - | |
| -- | electronic |
| -- | isbdmedia |
| 338 ## - | |
| -- | online resource |
| -- | rdacarrier |
| 588 ## - | |
| -- | Description based on PDF viewed 12/23/2015. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Neural networks (Computer science) |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Algorithms. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Artificial intelligence. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Machine learning. |
No items available.