|
|
|
|
LEADER |
01920cam a2200361 a 4500 |
001 |
74715 |
003 |
CZ ZlUTB |
005 |
20240911212116.0 |
006 |
m d |
007 |
cr un |
008 |
010226s1994 maua sb 001 0 eng d |
020 |
|
|
|a 9780262276863
|q (ebook)
|
035 |
|
|
|a (OCoLC)47009798
|z (OCoLC)827012857
|
040 |
|
|
|a N$T
|b eng
|c N$T
|d OCL
|d OCLCQ
|d YDXCP
|d OCLCQ
|d IEEEE
|d OCLCF
|
100 |
1 |
|
|a Kearns, Michael J.
|
245 |
1 |
3 |
|a An introduction to computational learning theory
|h [elektronický zdroj] /
|c Michael J. Kearns, Umesh V. Vazirani.
|
260 |
|
|
|a Cambridge, Mass. :
|b MIT Press,
|c c1994.
|
300 |
|
|
|a 1 online zdroj (xii, 207 p.) :
|b ill.
|
504 |
|
|
|a Includes bibliographical references (p. [193]-203) and index.
|
505 |
0 |
|
|a 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.
|
506 |
|
|
|a Plný text je dostupný pouze z IP adres počítačů Univerzity Tomáše Bati ve Zlíně nebo vzdáleným přístupem pro zaměstnance a studenty univerzity
|
650 |
|
0 |
|a Machine learning.
|
650 |
|
0 |
|a Artificial intelligence.
|
650 |
|
0 |
|a Algorithms.
|
650 |
|
0 |
|a Neural networks (Computer science)
|
655 |
|
7 |
|a elektronické knihy
|7 fd186907
|2 czenas
|
655 |
|
9 |
|a electronic books
|2 eczenas
|
700 |
1 |
|
|a Vazirani, Umesh Virkumar.
|
776 |
0 |
8 |
|i Print version:
|a Kearns, Michael J.
|t Introduction to computational learning theory.
|d Cambridge, Mass. : MIT Press, c1994
|z 0262111934
|w (DLC) 94016588
|w (OCoLC)30476515
|
856 |
4 |
0 |
|u https://proxy.k.utb.cz/login?url=http://ieeexplore.ieee.org/xpl/bkabstractplus.jsp?bkn=6267405
|y Plný text
|
992 |
|
|
|a BK
|c EBOOK-TN
|c MITPRESS
|
999 |
|
|
|c 74715
|d 74715
|
993 |
|
|
|x NEPOSILAT
|y EIZ
|