| Titre : | The elements of statistical learning : data mining, inference, and prediction | | Type de document : | texte imprimé | | Auteurs : | Trevor Hastie, Auteur; Robert Tibshirani, Auteur | | Mention d'édition : | second edition | | Editeur : | Springer Science + Business Media, Inc. | | Année de publication : | 2017 | | Importance : | xxii, 745 pages | | Présentation : | illustrations (some colors) | | Format : | 24 cm | | ISBN/ISSN/EAN : | 978-0-387-84857-0 | | Langues : | Anglais (eng) | | Catégories : | 000 Généralités:006.31 Machine Learning
| | Mots-clés : | The elements of statistical learning | | Index. décimale : | 006.312 | | Résumé : | "During the past decade there has been an explosion in computation and information technology. With it have come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics. Many of these tools have common underpinnings but are often expressed with different terminology. This book describes the important ideas in these areas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of color graphics."--Jacket |
The elements of statistical learning [texte imprimé] : data mining, inference, and prediction / Trevor Hastie, Auteur; Robert Tibshirani, Auteur . - second edition . - [S.l.] : Springer Science + Business Media, Inc., 2017 . - xxii, 745 pages : illustrations (some colors) ; 24 cm. ISBN : 978-0-387-84857-0 Langues : Anglais ( eng) | Catégories : | 000 Généralités:006.31 Machine Learning
| | Mots-clés : | The elements of statistical learning | | Index. décimale : | 006.312 | | Résumé : | "During the past decade there has been an explosion in computation and information technology. With it have come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics. Many of these tools have common underpinnings but are often expressed with different terminology. This book describes the important ideas in these areas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of color graphics."--Jacket |
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