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|a 0-262-11193-4
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|a AR-CdUBP
|b spa
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| 041 |
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|a eng
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| 100 |
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|a Kearns, Michael J.
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| 245 |
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|a An introduction to computational learning theory /
|c Michael J. Kearns, Umesh V. Vazirani
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| 260 |
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|a Cambridge ; London :
|b MIT,
|c 1994
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| 300 |
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|a 207 p. ;
|c 20 cm.
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| 504 |
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|a Bibliografía: p. 194-203
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|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.
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| 650 |
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4 |
|a INTELIGENCIA ARTIFICIAL
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| 650 |
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4 |
|a REDES NEURONALES
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| 650 |
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4 |
|a ALGORITMOS
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| 653 |
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|a INFORMATICA
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| 700 |
1 |
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|a Vazirani, Umesh V.
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| 930 |
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|a INFORMATICA
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| 931 |
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|a 01839
|b UBP
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| 942 |
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|2 cdu
|c BK
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|a SMM
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|a 004.89
|b K214
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|c 17454
|d 17454
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