The elements of statistical learning : data mining, inference, and prediction /
Hastie, Trevor,
The elements of statistical learning : data mining, inference, and prediction / Trevor Hastie, Robert Tibshirani, Jerome Friedman. - 2a edición, 7a reimpresión corregida - 1 online resource (xxii, 745 pages) : color illustrations - Springer series in statistics, .
Second edition corrected at 7th printing in 2013.
Incluye referencias bibliográficas e index (páginas 699-727)
Introduction -- Overview of supervised learning -- Linear methods for regression -- Linear methods for classification -- Basis expansions and regularization -- Kernel smoothing methods -- Model assessment and selection -- Model inference and averaging -- Additive models, trees, and related methods -- Boosting and additive trees -- Neural networks -- Support vector machines and flexible discriminants -- Prototype methods and nearest-neighbors -- Unsupervised learning -- Random forests -- Ensemble learning -- Undirected graphical models -- High-dimensional problems: p>> N. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18.
"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.
9780387848587 0387848584 9781282126749 1282126741
9786612126741
978-0-387-84857-0 Springer http://www.springerlink.com
Aprendizaje Automático Supervisado
Procesamiento Automatizado de Datos
Estadísticas
Biología ---Procesamiento Automatizado de Datos
Biología Computacional
Matemáticas ---Procesamiento Automatizado de Datos
Libros electrónicos
Q325.75 / .H37 2009eb
006.3/1 22
The elements of statistical learning : data mining, inference, and prediction / Trevor Hastie, Robert Tibshirani, Jerome Friedman. - 2a edición, 7a reimpresión corregida - 1 online resource (xxii, 745 pages) : color illustrations - Springer series in statistics, .
Second edition corrected at 7th printing in 2013.
Incluye referencias bibliográficas e index (páginas 699-727)
Introduction -- Overview of supervised learning -- Linear methods for regression -- Linear methods for classification -- Basis expansions and regularization -- Kernel smoothing methods -- Model assessment and selection -- Model inference and averaging -- Additive models, trees, and related methods -- Boosting and additive trees -- Neural networks -- Support vector machines and flexible discriminants -- Prototype methods and nearest-neighbors -- Unsupervised learning -- Random forests -- Ensemble learning -- Undirected graphical models -- High-dimensional problems: p>> N. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18.
"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.
9780387848587 0387848584 9781282126749 1282126741
9786612126741
978-0-387-84857-0 Springer http://www.springerlink.com
Aprendizaje Automático Supervisado
Procesamiento Automatizado de Datos
Estadísticas
Biología ---Procesamiento Automatizado de Datos
Biología Computacional
Matemáticas ---Procesamiento Automatizado de Datos
Libros electrónicos
Q325.75 / .H37 2009eb
006.3/1 22