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Titre :
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Pattern recognition and machine learning
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Auteurs :
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Christopher M. Bishop, Auteur
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Type de document :
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texte imprimé
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Editeur :
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Paris ; Berlin ; Heidelberg [etc] : Springer, 2009
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ISBN/ISSN/EAN :
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978-0-387-31073-2
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Format :
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1 vol. (XX-738 p.) / ill. en coul., couv. ill. en coul. / 25 cm
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Note générale :
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Bibliogr. p. 711-728. Index
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Langues:
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Anglais
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Index. décimale :
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Informatique - Maths et Statistiques
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Catégories :
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[Archirès ] 041 Méthodologie > Méthodologie > Méthodologie du projet > Processus de conception > Expérimentation > Modélisation
[Archirès ] 099 Mots outil > Statistique
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Résumé :
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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. [Source : 4e de couv.]
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Note de contenu :
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Contient des exercices en fin de chapitres, et un choix de documents en appendice
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