Parameter
- 745 Seiten
- 27 Lesestunden
Mehr zum Buch
In the past decade, the surge in computation and information technology has generated vast amounts of data across various fields, including medicine, biology, finance, and marketing. This challenge has prompted the development of new statistical tools and the emergence of areas like data mining, machine learning, and bioinformatics. While these tools share common foundations, they often use different terminologies. This book presents key concepts in these domains within a unified framework, focusing on ideas rather than mathematical complexities. It includes numerous examples and colorful graphics, making it a valuable resource for statisticians and anyone interested in data mining in science or industry. The content covers a wide range of topics, from supervised to unsupervised learning, including neural networks, support vector machines, classification trees, and boosting. This new edition introduces several topics absent from the original, such as graphical models, random forests, ensemble methods, least angle regression, non-negative matrix factorization, and spectral clustering. Additionally, it addresses methods for "wide" data scenarios, including multiple testing and false discovery rates.
Buchkauf
The Elements of Statistical Learning, Second Edition, Trevor Hastie
- Sprache
- Erscheinungsdatum
- 2009
- product-detail.submit-box.info.binding
- (Hardcover)
Hier könnte deine Bewertung stehen.
- Titel
- The Elements of Statistical Learning, Second Edition
- Sprache
- Englisch
- Autor*innen
- Trevor Hastie
- Verlag
- Springer Berlin
- Erscheinungsdatum
- 2009
- Einband
- Hardcover
- Seitenzahl
- 745
- ISBN10
- 0387848576
- ISBN13
- 9780387848570
- Reihe
- Schlagwörter
- Sachbücher, Lehrbücher, Technologie & Industrie, Computer & Internet, Wissenschaft, Technologie, Mathematik-Lehrbücher, Künstliche Intelligenz
- Bewertung
- 4,4 von 5 Sternen
- Beschreibung
- In the past decade, the surge in computation and information technology has generated vast amounts of data across various fields, including medicine, biology, finance, and marketing. This challenge has prompted the development of new statistical tools and the emergence of areas like data mining, machine learning, and bioinformatics. While these tools share common foundations, they often use different terminologies. This book presents key concepts in these domains within a unified framework, focusing on ideas rather than mathematical complexities. It includes numerous examples and colorful graphics, making it a valuable resource for statisticians and anyone interested in data mining in science or industry. The content covers a wide range of topics, from supervised to unsupervised learning, including neural networks, support vector machines, classification trees, and boosting. This new edition introduces several topics absent from the original, such as graphical models, random forests, ensemble methods, least angle regression, non-negative matrix factorization, and spectral clustering. Additionally, it addresses methods for "wide" data scenarios, including multiple testing and false discovery rates.