Catálogo Bibliográfico
Búsqueda por número de registro: 017614
| LDR | ·····cam##22·····7a#4500 |
| 001 | 017614 |
| 005 | 20230321152641.0 |
| 008 | 060216s2006####nyua###f#b####001#0#eng#d |
| 245 | 10 | $a Pattern recognition and machine learning / $c Christopher M. Bishop. |
| 260 | ## | $a New York : $b Springer, $c 2006. |
| 300 | ## | $a xx, 738 p. : $b il. (algunas col.) ; $c 25 cm. |
| 490 | 0# | $a Information science and statistics |
| 504 | ## | $a Incluye referencias bibliográficas e índice. |
| 520 | ## | $a This is the first textbook on pattern recognition to present the Bayesian viewpoint. The book presents approximate inference algorithms that permit fast approximate answers in situations where exact answers are not feasible. It uses graphical models to describe probability distributions when no other books apply graphical models to machine learning. No previous knowledge of pattern recognition or machine learning concepts is assumed. Familiarity with multivariate calculus and basic linear algebra is required, and some experience in the use of probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory. |
| 020 | ## | $a 0387310738 |
| 020 | ## | $a 9780387310732 |
| 100 | 1# | $a Bishop, Christopher M. |
| 080 | ## | $a 681.3:519.688 |
| 082 | 00 | $a 006.4 $2 22 |
| 650 | #7 | $a Pattern recognition. $2 inist |
| 650 | #7 | $a Data processing. $2 inist |
| 650 | #7 | $a Reconocimiento de formas. $2 inist |
| 650 | #7 | $a Tratamiento de la información. $2 inist |
| 010 | ## | $a ##2006922522 |
| 016 | 7# | $a 977347575 $2 GyFmDB |
| 040 | ## | $a OHX $c OHX $d PU $d DLC |
| 050 | 00 | $a Q327 $b .B52 2006 |
| 856 | 41 | $u https://m.media-amazon.com/images/I/51K2ZLj+NuL.%5FSY344%5FBO1,204,203,200%5F.jpg $3 Tapa |
| 856 | 41 | $u http://campi.cab.cnea.gov.ar/tocs/24701.pdf $3 Indice |
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Este catálogo cuenta con
18.656 registros,
y fue actualizado el 16/05/2024 a las 02:15
OPACMARC
2008.12.01
· Desarrollado en la Biblioteca Dr. Antonio Monteiro, Instituto de Matemática de
Bahía Blanca (CONICET/UNS).
Este módulo integra el Sistema 