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

• Main Authors: Hastie, Trevor (Author), Tibshirani, Robert (Author), Friedman, Jerome (Author)
• Corporate Author: SpringerLink (Online service)
• Format: Electronic eBook
• Language: English
• Published: New York, NY : Springer New York : Imprint: Springer, 2009.
• Edition: Second Edition.
• Series: Springer Series in Statistics,
• Subjects: Computer science.; Data mining.; Artificial intelligence.; Bioinformatics.; Computational biology.; Probabilities.; Statistics.; Computer Science.; Artificial Intelligence (incl. Robotics).; Data Mining and Knowledge Discovery.; Probability Theory and Stochastic Processes.; Statistical Theory and Methods.; Computational Biology/Bioinformatics.; Computer Appl. in Life Sciences.

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...

Table of Contents:

• 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.