稀疏統計學習-LASSO方法及其推廣 (英文) 9787523201329 特雷弗.哈斯蒂羅伯特.蒂布希拉尼

Name: 稀疏統計學習-LASSO方法及其推廣 (英文) 9787523201329 特雷弗.哈斯蒂 羅伯特.蒂布希拉尼
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書名：稀疏統計學習-LASSO方法及其推廣 (英文)
ISBN：9787523201329
出版社：世界圖書出版有限公司
著編譯者：特雷弗.哈斯蒂羅伯特.蒂布希拉尼
頁數：351
所在地：中國大陸 *此為代購商品
書號：1583022
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【台灣高等教育出版社簡體書】稀疏統計學習-LASSO方法及其推廣 (英文) 787523201329 特雷弗.哈斯蒂羅伯特.蒂布希拉尼

內容簡介

稀疏統計模型只具有少數非零參數或權重，經典地體現了化繁為簡的理念，因而廣泛應用於諸多領域。本書就稀疏性統計學習做出總結，以LASSO方法為中心，層層推進，逐漸囊括其他方法，深入探討諸多稀疏性問題的求解和應用；不僅包含大量的例子和清晰的圖表，還附有文獻註釋和課後練習，是深入學習統計學知識的參考。本書適合計算機科學、統計學和機器學習的學生和研究人員。

Preface
1 Introduction
2 The Lasso for Linear Models
2 1 Introduction
2 2 The Lasso Estimator
2 3 Cross-Validation and Inference
2 4 Computation of the Lasso Solution
2 4 1 Single Predictor: Soft Thresholding
2 4 2 Multiple Predictors: Cyclic Coordinate Descent
2 4 3 Soft-Thresholding and Orthogonal Bases
2 5 Degrees of Freedom
2 6 Uniqueness of the Lasso Solutions
2 7 A Glimpse at the Theory
2 8 The Nonnegative Garrote
2 9 lq Penalties and Bayes Estimates
2 10 Some Perspective
Exercises
3 Generalized Linear Models
3 1 Introduction
3 2 Logistic Regression
3 2 1 Example: Document Classification
3 2 2 Algorithms
3 3 Multiclass Logistic Regression
3 3 1 Example: Handwritten Digits
3 3 2 Algorithms
3 3 3 Grouped-Lasso Multinomial
3 4 Log-Linear Models and the Poisson GLM
3 4 1 Example: Distribution Smoothing
3 5 Cox Proportional Hazards Models
3 5 1 Cross-Validation
3 5 2 Pre-Validation
3 6 Support Vector Machines
3 6 1 Logistic Regression with Separable Data
3 7 Computational Details and glmnet
Bibliographic Notes
Exercises
4 Generalizations of the Lasso Penalty
4 1 Introduction
4 2 The Elastic Net
4 3 The Group Lasso
4 3 1 Computation for the Group Lasso
4 3 2 Sparse Group Lasso
4 3 3 The Overlap Group Lasso
4 4 Sparse Additive Models and the Group Lasso
4 4 1 Additive Models and Backfitting
4 4 2 Sparse Additive Models and Backfitting
4 4 3 Approaches Using Optimization and the Group Lasso
4 4 4 Multiple Penalization for Sparse Additive Models
4 5 The Fused Lasso
4 5 1 Fitting the Fused Lasso
4 5 1 1 Reparametrization
4 5 1 2 A Path Algorithm
4 5 1 3 A Dual Path Algorithm
4 5 1 4 Dynamic Programming for the Fused Lasso
4 5 2 Trend Filtering
4 5 3 Nearly Isotonic Regression
4 6 Nonconvex Penalties
Bibliographic Notes
Exercises
5 Optimization Methods
5 1 Introduction
5 2 Convex Optimality Conditions
5 2 1 Optimality for Differentiable Problems
5 2 2 Nondifferentiable Functions and Subgradients
5 3 Gradient Descent
5 3 1 Unconstrained Gradient Descent
5 3 2 Projected Gradient Methods
5 3 3 Proximal Gradient Methods
5 3 4 Accelerated Gradient Methods
5 4 Coordinate Descent
5 4 1 Separability and Coordinate Descent
5 4 2 Linear Regression and the Lasso
5 4 3 Logistic Regression and Generalized Linear Models
5 5 A Simulation Study
5 6 Least Angle Regression
5 7 Alternating Direction Method of Multipliers
5 8 Minorization-Maximization Algorithms
5 9 Biconvexity and Alternating Minimization
5 10 Screening Rules
Bibliographic Notes
Appendix
Exercises
6 Statistical Inference
6 1 The Bayesian Lasso
6 2 The Bootstrap
6 3 Post-Selection Inference for the Lasso
6 3 1 The Covariance Test
6 3 2 A General Scheme for Post-Selection Inference
6 3 2 1 Fixed-入 Inference for the Lasso
6 3 2 2 The Spacing Test for LAR
6 3 3 What Hypothesis Is Being Tested?
6 3 4 Back to Forward Stepwise Regression
6 4 Inference via a Debiased Lasso
6 5 Other Proposals for Post-Selection Inference
Bibliographic Notes
Exercises
7 Matrix Decompositions， Approximations， and Completion
7 1 Introduction
7 2 The Singular Value Decomposition
7 3 Missing Data and Matrix Completion
7 3 1 The Netflix Movie Challenge
7 3 2 Matrix Completion Using Nuclear Norm
7 3 3 Theoretical Results for Matrix Completion
7 3 4 Maximum Margin Factorization and Related Methods
7 4 Reduced-Rank Regression
7 5 A General Matrix Regression Framework
7 6 Penalized Matrix Decomposition
7 7 Additive Matrix Decomposition
Bibliographic Notes
Exercises
8 Sparse Multivariate Methods
8 1 Introduct
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