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The Elements of Statistical Learning

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Cover of 'The Elements of Statistical Learning'

Table of Contents

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    Book Overview
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    Chapter 1 Introduction
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    Chapter 2 Overview of Supervised Learning
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    Chapter 3 Linear Methods for Regression
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    Chapter 4 Linear Methods for Classification
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    Chapter 5 Basis Expansions and Regularization
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    Chapter 6 Kernel Smoothing Methods
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    Chapter 7 Model Assessment and Selection
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    Chapter 8 Model Inference and Averaging
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    Chapter 9 Additive Models, Trees, and Related Methods
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    Chapter 10 Boosting and Additive Trees
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    Chapter 11 Neural Networks
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    Chapter 12 Support Vector Machines and Flexible Discriminants
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    Chapter 13 Prototype Methods and Nearest-Neighbors
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    Chapter 14 Unsupervised Learning
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    Chapter 15 Random Forests
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    Chapter 16 Ensemble Learning
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    Chapter 17 Undirected Graphical Models
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    Chapter 18 High-Dimensional Problems: p N
Attention for Chapter 4: Linear Methods for Classification
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