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01526cam a2200325 a 4500 |
| 001 |
c000213272 |
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20060207104750.0 |
| 008 |
940426s1994 maua b 001 0 eng |
| 010 |
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|a 94016588
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| 019 |
1 |
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|a 10942506
|5 LACONCORD2021
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| 020 |
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|a 0262111934
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| 035 |
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|a (OCoLC)30476515
|5 LACONCORD2021
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| 040 |
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|a TOC
|b eng
|c TOC
|d NU
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| 050 |
0 |
0 |
|a Q325.5
|b .K44 1994
|
| 082 |
0 |
0 |
|a 006.3
|2 20
|
| 100 |
1 |
|
|a Kearns, Michael J.
|
| 245 |
1 |
3 |
|a An introduction to computational learning theory /
|c Michael J. Kearns, Umesh V. Vazirani.
|
| 260 |
|
|
|a Cambridge, Mass. :
|b MIT Press,
|c c1994.
|
| 300 |
|
|
|a xii, 207 p. :
|b ill. ;
|c 24 cm.
|
| 504 |
|
|
|a Includes bibliographical references (p. [193]-203) and index.
|
| 505 |
0 |
|
|a 1. The Probably Approximately Correct Learning Model -- 2. Occam's Razor -- 3. The Vapnik-Chervonenkis Dimension -- 4. Weak and Strong Learning -- 5. Learning in the Presence of Noise -- 6. Inherent Unpredictability -- 7. Reducibility in PAC Learning -- 8. Learning Finite Automata by Experimentation -- 9. Appendix: Some Tools for Probabilistic Analysis.
|
| 650 |
|
0 |
|a Machine learning.
|
| 650 |
|
0 |
|a Artificial intelligence.
|
| 650 |
|
0 |
|a Algorithms.
|
| 650 |
|
0 |
|a Neural networks (Computer science)
|
| 700 |
1 |
|
|a Vazirani, Umesh Virkumar.
|
| 852 |
8 |
|
|b CARM
|h A2:AM09G0
|i C07935
|p 0273411
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| 952 |
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|p Can circulate
|a CAVAL
|b CAVAL
|c CAVAL
|d CARM 1 Store
|e C07935
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|m 0273411
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