統計學習基礎-機器學習中的數據挖掘.推斷與預測 (第2版) (英文) 9787519296865 特雷弗.哈斯蒂羅伯特

Name: 統計學習基礎-機器學習中的數據挖掘.推斷與預測 (第2版) (英文) 9787519296865 特雷弗.哈斯蒂 羅伯特
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書名：統計學習基礎-機器學習中的數據挖掘.推斷與預測 (第2版) (英文)
ISBN：9787519296865
出版社：世界圖書出版公司
著編譯者：特雷弗.哈斯蒂羅伯特.蒂布希拉尼
頁數：745
所在地：中國大陸 *此為代購商品
書號：1583021
可大量預訂，請先連絡。

【台灣高等教育出版社簡體書】統計學習基礎-機器學習中的數據挖掘.推斷與預測 (第2版) (英文) 787519296865 特雷弗.哈斯蒂羅伯特.蒂布希拉尼

內容簡介

隨著計算機和信息技術迅猛發展，醫學、生物學、金融、以及市場等各個領域的大量數據的產生，處理這些數據以及挖掘它們之間的關係對於一個統計工作者顯得尤為重要。本書運用共同的理論框架將這些領域的重要觀點做了很好的闡釋，重點強調方法和概念基礎而非理論性質，運用統計的方法更是突出概念而非數學。另外，書中大量的彩色圖例可以幫助讀者更好地理解概念和理論。

Preface to the Second Edition
Preface to the First Edition
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 Neighbors
2 3 1 Linear Models and Least Squares
2 3 2 Nearest-Neighbor Methods
2 3 3 From Least Squares to Nearest Neighbors
2 4 Statistical Decision Theory
2 5 Local Methods in High Dimensions
2 6 Statistical Models， Supervised Learning and Function Approximation
2 6 1 A Statistical Model for the Joint Distribution Pr(X， Y)
2 6 2 Supervised Learning
2 6 3 Function Approximation
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 2 3 Multiple Regression from Simple Univariate Regression
3 2 4 Multiple Outputs
3 3 Subset Selection
3 3 1 Best-Subset Selection
3 3 2 Forward- and Backward-Stepwise Selection
3 3 3 Forward-Stagewise Regression
3 3 4 Prostate Cancer Data Example (Continued)
3 4 Shrinkage Methods
3 4 1 Ridge Regression
3 4 2 The Lasso
3 4 3 Discussion: Subset Selection， Ridge Regression and the Lasso
3 4 4 Least Angle Regression
3 5 Methods Using Derived Input Directions
3 5 1 Principal Components Regression
3 5 2 Partial Least Squares
3 6 Discussion: A Comparison of the Selection and Shrinkage Methods
3 7 Multiple Outcome Shrinkage and Selection
3 8 More on the Lasso and Related Path Algorithms
3 8 1 Incremental Forward Stagewise Regression
3 8 2 Piecewise-Linear Path Algorithms
3 8 3 The Dantzig Selector
3 8 4 The Grouped Lasso
3 8 5 Further Properties of the Lasso
3 8 6 Pathwise Coordinate Optimization
3 9 Computational Considerations
Bibliographic Notes
Exercises
4 Linear Methods for Classification
5 Basis Expansions and Regularization
6 Kernel Smoothing 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 Flexible Discriminants
13 Prototype Methods and Nearest-Neighbors
14 Unsupervised Learning
15 Random Forests
16 Ensemble Learning
17 Undirected Graphical Models
18 High-Dimensional Problems: p>>N
References
Author Index
Index

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