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...

وصف كامل

التفاصيل البيبلوغرافية
المؤلف الرئيسي: Hastie, Trevor
مؤلفون آخرون: Tibshirani, Robert, Friedman, J. H.
التنسيق: Printed Book
منشور في: New York, NY : Springer, c2009.
الطبعة:2nd ed.
سلاسل:Springer series in statistics,
الموضوعات:
Machine learning.
Statistics
Data mining.
Bioinformatics.
Inference.
Forecasting.
Computational intelligence.
جدول المحتويات:
  • 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.

مواد مشابهة