The elements of statistical learning: Data Mining, Inference, and Prediction

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"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 th...

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Detalhes bibliográficos
Autor principal: Hastie, Trevor
Outros Autores: Tibshirani, Robert, Friedman, J. H.
Formato: Printed Book
Publicado em: New York, NY : Springer, c2009.
Edição:2nd ed.
coleção:Springer series in statistics,
Assuntos:
Machine learning.
Statistics
Data mining.
Bioinformatics.
Inference.
Forecasting.
Computational intelligence.
Sumário:
  • 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 nearest-neighbors
  • 14. Unsupervised learning
  • 15. Random forests
  • 16. Ensemble learning
  • 17. Undirected graphical models
  • 18. High-dimensional problems: p>> N.

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