Description 
1 online resource (xxii, 745 pages) : color illustrations 
Series 
Springer series in statistics, 01727397 

Springer series in statistics. 01727397

Contents 
1. Introduction  2. Overview of supervised learning  3. Linear methods for regression  4. Linear methods for classification  5. Basis expansions and regularization  6. Kernel smoothing methods  7. Model assessment and selection  8. Model inference and averaging  9. Additive models, trees, and related methods  10. Boosting and additive trees  11. Neural networks  12. Support vector machines and flexible discriminants  13. Prototype methods and nearestneighbors  14. Unsupervised learning  15. Random forests  16. Ensemble learning  17. Undirected graphical models  18. Highdimensional problems: p>> N 
Summary 
"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 
Notes 
Second edition corrected at 7th printing in 2013 
Bibliography 
Includes bibliographical references (pages 699727) and indexes 
Notes 
Online resource and print version record. SpringerLink (viewed September 29, 2014) 
Subject 
Supervised learning (Machine learning)


Electronic data processing.


Statistics.


Biology  Data processing.


Computational biology.


Mathematics  Data processing.


Data mining.


Bioinformatics.


Statistics as Topic


Computational Biology


Mathematical Computing


Data Mining


COMPUTERS  Database Management  Data Mining.


Supervised learning (Machine learning)


Bioinformatics.


Supervised learning (Machine learning)


Electronic data processing.


Statistics.


Biology  Data processing.


Computational biology.


Mathematics  Data processing.


Data mining.


Maschinelles Lernen


Statistik


Machinelearning.


Datamining.


Prognoses.


Estatística computacional.


Estatística.


Mineração de dados.


Inferência estatística.

Form 
Electronic book

Author 
Tibshirani, Robert, author.


Friedman, J. H. (Jerome H.), author.

ISBN 
9780387848587 

0387848584 

9781282126749 

1282126741 
