9787510084508/统计学习基础 (第2版)(英文版)
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作者[德]黑斯蒂（Hastie T.） 著
出版社世界图书出版公司
ISBN9787510084508
出版时间2015-01
版次2
装帧平装
开本24开
纸张胶版纸
页数745页
定价119元
货号库存
上书时间2019-05-22
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基本信息
书名:统计学习基础 (第2版)(英文版)
定价：119.00元
作者:(德)黑斯蒂 著
出版社：世界图书出版公司
出版日期：2015-01-01
ISBN：9787510084508
字数：
页码：
装帧：平装
开本：[Format
内容简介：
本书是Springer统计系列丛书之一，旨在让读者深入了解数据挖掘和预测。
随着计算机和信息技术迅猛发展，医学、生物学、金融、以及市场等各个领域的大量数据的产生，处理这些数据以及挖掘它们之间的关系对于一个统计工作者显得尤为重要。本书运用共同的理论框架将这些领域的重要观点做了很好的阐释，重点强调方法和概念基础而非理论性质，运用统计的方法更是突出概念而非数学。另外，书中大量的彩色图例可以帮助读者更好地理解概念和理论。
目次:导论; 监督学习概述; 线性回归模型; 线性分类方法; 基展开与正则性; 核方法; 模型评估与选择; 模型参考与平均; 可加性模型，树与相关方法; 神经网络; 支持向量机器与弹性准则; 原型法和*近邻居; 无监督学习。
作者简介：
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目录：
Preface to the Second Edition
Preface to the First Edition
1Introduction
2Overview of Supervised Learning
2.1Introduction
2.2Variable Types and Terminology
2.3Two Simple Approaches to Prediction LeastSquares and Nearest Neighbors
2.3.1Linear Models and Least Squares
2.3.2Nearest-Neighbor Methods
2.3.3From Least Squares to Nearest Neighbors
2.4Statistical Decision Theory
2.5Local Methods in High Dimensions
2.6Statistical Models, Supervised Learning and Function Approximation
2.6.1A Statistical Model for the Joint Distribution Pr(X,Y)Preface to the Second Edition
Preface to the First Edition
1Introduction
2Overview of Supervised Learning
2.1Introduction
2.2Variable Types and Terminology
2.3Two Simple Approaches to Prediction LeastSquares and Nearest Neighbors
2.3.1Linear Models and Least Squares
2.3.2Nearest-Neighbor Methods
2.3.3From Least Squares to Nearest Neighbors
2.4Statistical Decision Theory
2.5Local Methods in High Dimensions
2.6Statistical Models, Supervised Learning and Function Approximation
2.6.1A Statistical Model for the Joint Distribution Pr(X,Y)
2.6.2Supervised Learning
2.6.3Function Approximation
2.7Structured Regression Models
2.7.1Difficulty of the Problem
2.8Classes of Restricted Estimators
2.8.1Roughness Penalty and Bayesian Methods
2.8.2Kernel Methods and Local Regression
2.8.3Basis Functions and Dictionary Methods
2.9Model Selection and the Bias-Variance rlyadeoff Bibliographic Notes
Exercises
3Linear Methods for Regression
3.1Introduction
3.2Linear Regression Models and Least Squares
3.2.1Example: Prostate Cancer
3.2.2The Gauss-Markov Theorem
3.2.3Multiple Regression from Simple Univariate Regression
3.2.4Multiple Outputs
3.3Subset Selection
3.3.1Best-Subset Selection
3.3.2Forward- and Backward-Stepwise Selection
3.3.3Forward-Stagewise Regression
3.3.4Prostate Cancer Data Example (Continued)
3.4Shrinkage Methods
3.4.1Ridge Regression
3.4.2The Lasso
3.4.3Discussion: Subset Selection, Ridge Regression and the Lasso
3.4.4Least Angle Regression
3.5Methods Using Derived Input Directions
3.5.1Principal Components Regression
3.5.2Partial Least Squares
3.6Discussion: A Comparison of the Selection and Shrinkage Methods
3.7Multiple Out*e Shrinkage and Selection
3.8More on the Lasso and Related Path Algorithms
3.8.1Incremental Forward Stagewise Regression
3.8.2Piecewise-Linear Path Algorithms
3.8.3The Dantzig Selector
3.8.4The Grouped Lasso
3.8.5Further Properties of the Lasso
3.8.6Pathwise Coordinate Optimization
3.9Computational Considerations Bibliographic Notes
Exercises
……
4Linear Methods for Classification
5Basis Expansions and Regularization
6Kernel Smoothing Methods
7Model Assessment and Selection
8Modellnference and Averaging
9Additive Models, Trees, and Related Methods
10Boosting and Additive Trees
11Neural Networks
12Support Vector Machines and Flexible Discriminants
13Prototype Methods and Nearest-Neighbors
14Unsupervised Learning
15Random Forests
16Ensemble Learning
17Undirected Graphical Models
18High-Dimensional Problems: p≥N
References
Author Index
Index
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