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A textbook by Christopher Bishop on pattern recognition and machine learning, covering probabilistic graphical models and Bayesian perspective. Download the PDF or access the exercises and solutions on Bishop's page.
- Christopher M. Bishop
- 2006
Authors: Christopher M. Bishop. First text on pattern recognition to present the Bayesian viewpoint, one that has become increasing popular in the last five years. Presents approximate inference algorithms that permit fast approximate answers in situations where exact answers are not feasible.
Compre online Pattern Recognition and Machine Learning, de Bishop, Christopher M. na Amazon. Frete GRÁTIS em milhares de produtos com o Amazon Prime. Encontre diversos livros escritos por Bishop, Christopher M. com ótimos preços.
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Pattern recognition has its origins in engineering, whereas machine learning grew out of computer science. However, these activities can be viewed as two facets of the same field, and together they have undergone substantial development over the pasttenyears.
- 7MB
- 746
23 de nov. de 1995 · This book provides the first comprehensive treatment of feed-forward neural networks from the perspective of statistical pattern recognition. After introducing the basic concepts of pattern recognition, the book describes techniques for modelling probability density functions, and discusses the properties and relative merits of the ...
- Christopher M Bishop
13 de mai. de 2023 · Textbook for graduates. Includes bibliographical references (p. 711-728) and index. 1. Introduction -- 2. Probability distributions -- 3. Linear models for regression -- 4. Linear models for classification -- 5. Neural networks -- 6. Kernel methods -- 7.
Microsoft
