Pattern recognition and machine learning
"The dramatic growth in practical applications for machine learning over the last ten years has been accompanied by many important developments in the underlying algorithms and techniques. For example, Bayesian methods have grown from a specialist niche to become mainstream, while graphical mod...
Sparad:
| Huvudupphovsman: | |
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| Materialtyp: | Livre papier |
| Språk: | Anglais |
| Publicerad: |
New York :
Springer
C 2006.
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| Serie: | Information science and statistics
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| Ämnen: | |
| Anmärkning: |
Liste des errata disponible sur le site https://www.microsoft.com/en-us/research/wp-content/uploads/2016/05/prml-errata-1st-20110921.pdf Contient des exercices en fin de chapitres, et un choix de documents en appendice. |
| Autres localisations: | Voir dans le Sudoc |
| Edition sous un autre format: | • Pattern recognition and machine learning, by Christopher M. Bishop, 1st ed. 2006, 2006, New York, Springer, Information Science and Statistics, 978-0-387-45528-0 |
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| 024 | |a 9781493938438 | ||
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| 100 | 1 | |a Bishop, Christopher M., |d 1959-...., |c chercheur en informatique. | |
| 245 | 1 | 0 | |a Pattern recognition and machine learning |c Christopher M. Bishop. |
| 260 | |a New York : |b Springer. | ||
| 260 | |c C 2006. | ||
| 300 | |a 1 volume (xx-738 pages) : |b illustrations en couleurs, couverture illustrée en couleurs ; |c 25 cm. | ||
| 490 | 1 | |a Information science and statistics | |
| 500 | |a Liste des errata disponible sur le site https://www.microsoft.com/en-us/research/wp-content/uploads/2016/05/prml-errata-1st-20110921.pdf | ||
| 500 | |a Contient des exercices en fin de chapitres, et un choix de documents en appendice. | ||
| 504 | |a Bibliographie pages 711-728. Index | ||
| 505 | 0 | |a 1. Introduction -- 2. Probability distributions -- 3. Linear models for regression -- 4. Linear models for classification -- 5. Neural networks -- 6. Kernel methods -- 7. Sparse Kernel machines -- 8. Graphical models -- 9. Mixture models and EM -- 10. Approximate inference -- 11. Sampling methods -- 12. Continuous latent variables -- 13. Sequential data -- 14. Combining models. | |
| 520 | |a "The dramatic growth in practical applications for machine learning over the last ten years has been accompanied by many important developments in the underlying algorithms and techniques. For example, Bayesian methods have grown from a specialist niche to become mainstream, while graphical models have emerged as a general framework for describing and applying probabilistic techniques. The practical applicability of Bayesian methods has been greatly enhanced by the development of a range of approximate inference algorithms such as variational Bayes and expectation propagation, while new models based on kernels have had a significant impact on both algorithms and applications." | ||
| 650 | |a Perception des structures | ||
| 650 | |a Reconnaissance des formes (informatique) | ||
| 650 | |a Apprentissage automatique | ||
| 650 | |a Modèles linéaires (statistique) | ||
| 776 | 0 | |0 123110092 |t Pattern recognition and machine learning |f by Christopher M. Bishop |e 1st ed. 2006 |d 2006 |c New York |n Springer |s Information Science and Statistics |z 978-0-387-45528-0 | |
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