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|a 0262111934
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|a AR-SrUBC
|b spa
|e rcaa2
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|a 004.85=20
|2 2000 ES
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| 100 |
1 |
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|a Kearns, Michael J.
|9 45674
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| 245 |
1 |
0 |
|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, Mass. :
|b MIT Press,
|c c1994.
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| 300 |
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|a xii, 207 p. :
|b il. ;
|c 24 cm.
|
| 336 |
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|a texto
|2 rdacontent
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| 337 |
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|a sin mediación
|2 rdamedia
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| 338 |
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|a volumen
|2 rdacarrier
|
| 505 |
0 |
0 |
|a Contenido: 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.
|
| 650 |
|
7 |
|a ALGORITMOS
|2 lemb3
|9 2773
|
| 650 |
|
7 |
|a REDES NEURALES (COMPUTADORES)
|2 lemb3
|9 45675
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| 650 |
|
7 |
|a INTELIGENCIA ARTIFICIAL
|2 lemb3
|9 2548
|
| 700 |
1 |
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|a Vazirani, Umesh V.
|9 45676
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| 942 |
|
|
|2 cdu
|b 2001-01-13
|c BK
|d 025728
|h 004.85=20
|i KEAi
|z MI
|6 0048520_KEAI
|
| 999 |
|
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|c 23903
|d 23903
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