The Elements of Statistical Learning Data Mining, Inference, and Prediction, Second Edition /

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 fiel...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autores principales: Hastie, Trevor (Autor), Tibshirani, Robert (Autor), Friedman, Jerome (Autor)
Autor Corporativo: SpringerLink (Online service)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: New York, NY : Springer New York : Imprint: Springer, 2009.
Edición:2nd ed. 2009.
Colección:Springer Series in Statistics,
Temas:
Artificial intelligence.
Data mining.
Probabilities.
Statistics .
Bioinformatics.
Artificial Intelligence.
Data Mining and Knowledge Discovery.
Probability Theory.
Statistical Theory and Methods.
Computational and Systems Biology.
Acceso en línea:Texto Completo
Tabla de Contenidos:
  • 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.

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