统计学习基础 pdf下载

基本信息

书名：统计学习基础

定价：88.00元

作者：(美)哈斯蒂(Hastie,T)

出版社：世界图书出版公司

出版日期：2009-01-01

ISBN：9787506292313

字数：

页码：533

版次：1

装帧：平装

开本：32开

商品重量：0.001kg

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内容提要

The learning problems that we consider can be roughly categorized as either supervised or unsupervised. In supervised learning, the goal is to predict the value of an oute measure based on a number of input measures; in unsupervised learning, there is no oute measure, and the goal is to describe the associations and patterns among a set of input measures.

目录

Preface
1 Introduction
2 Overview of Supervised Learning
　2.1 Introduction
　2.2 Variable Types and Terminology
　2.3 Two Simple Approaches to Prediction: Least Squares and Nearest Neiors
　　2.3.1 Linear Models and Least Squares
　　2.3.2 Nearest-Neior Methods
　　2.3.3 From Least Squares to Nearest Neiors
　2.4 Statistical Decision Theory
　2.5 Local Methods in High Dimensions
　2.6 Statistical Models, Supervised Learning and Function Appromation
　　2.6.1 A Statistical Model for the Joint Distribution Pr(X,Y)
　　2.6.2 Supervised Learning
　　2.6.3 Function Appromation
　2.7 Structured Regression Models
　　2.7.1 Difficulty of the Problem
　2.8 Classes of Restricted Estimators
　　2.8.1 Roughness Penalty and Bayesian Methods
　　2.8.2 Kernel Methods and Local Regression
　　2.8.3 Basis Functions and Dictionary Methods
　2.9 Model Selection and the Bias-Variance Tradeoff
　Bibliographic Notes
　Exercises
3 Linear Methods for Regression
　3.1 Introduction
　3.2 Linear Regression Models and Least Squares
　　3.2.1 Example：Prostate Cancer
　　3.2.2 The Ganss-Markov Theorem
　3.3 Multiple Regression from Simple Univariate Regression
　　3.3.1 Multiple Outputs
　3.4 Subset Selection and Coefficient Shrinkage
　　3.4.1 Subset Selection
　　3.4.2 Prostate Cancer Data Example fContinued)
　　3.4.3 Shrinkage Methods
　　3.4.4 Methods Using Derived Input Directions
　　3.4.5 Discussion：A Comparison of the Selection and Shrinkage Methods
　　3.4.6 Multiple Oute Shrinkage and Selection
　3.5 Compntational Considerations
　Bibliographic Notes
　Exercises
4 Linear Methods for Classification
　4.1 Introduction
　4.2 Linear Regression of an Indicator Matrix
　4.3 Linear Discriminant Analysis
　　4.3.1 Regularized Discriminant Analysis
　　4.3.2 Computations for LDA
　　4.3.3 Reduced-Rank Linear Discriminant Analysis
　4.4 Logistic Regression
　　4.4.1 Fitting Logistic Regression Models
　　4.4.2 Example：South African Heart Disease
　　4.4.3 Quadratic Appromations and Inference
　　4.4.4 Logistic Regression or LDA7
　4.5 Separating Hyper nes
　　4.5.1 Rosenblatt's Perceptron Learning Algorithm
　　4.5.2 Optimal Separating Hyper nes
　Bibliographic Notes
　Exercises
5 Basis Expansions and Regularizatlon
　5.1 Introduction
　5.2 Piecewise Polynomials and Splines
　　5.2.1 Natural Cubic Splines
　　5.2.2 Example: South African Heart Disease (Continued)
　　5.2.3 Example: Phoneme Recognition
　5.3 Filtering and Feature Extraction
　5.4 Smoothing Splines
　　5.4.1 Degrees of Freedom and Smoother Matrices
　5.5 Automatic Selection of the Smoothing Parameters
　　5.5.1 Fing the Degrees of Freedom
　　5.5.2 The Bias-Variance Tradeoff
　5.6 Nonparametric Logistic Regression
　5.7 Multidimensional Splines
　5.8 Regularization and Reproducing Kernel Hilbert Spaces . .
　　5.8.1 Spaces of Phnctions Generated by Kernels
　　5.8.2 Examples of RKHS
　5.9 Wavelet Smoothing
　　5.9.1 Wavelet Bases and the Wavelet Transform
　　5.9.2 Adaptive Wavelet Filtering
　Bibliographic Notes
　Exercises
　Appendix: Computational Considerations for Splines
　Appendix: B-splines
　Appendix: Computations for Smoothing Splines
6 Kernel 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 Fleble Discriminants
13 Prototype Methods and Nearest-Neiors
14 Unsupervised Learning
References
Author Index
Index

作者介绍

作者：（德国）T.黑斯蒂（Trevor Hastie）

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