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The elements of statistical learning : data mining, inference, and prediction : with 200 full-color illustrations

Author: Trevor Hastie; Robert Tibshirani; J H Friedman
Publisher: New York : Springer, ©2001.
Series: Springer series in statistics.
Edition/Format:   eBook : Document : EnglishView all editions and formats
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Genre/Form: Livre électronique (Descripteur de forme)
Ressource Internet (Descripteur de forme)
Additional Physical Format: Print version:
Hastie, Trevor.
Elements of statistical learning.
New York : Springer, ©2001
(DLC) 2001031433
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Trevor Hastie; Robert Tibshirani; J H Friedman
OCLC Number: 424376381
Notes: Titre de l'écran-titre (visionné le 14 mai 2008).
Description: 1 ressource en ligne
Contents: Cover --
Table of Contents --
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 Neighbors --
2.4 Statistical Decision Theory --
2.5 Local Methods in High Dimensions --
2.6 Statistical Models, Supervised Learning and Function Approximation --
2.7 Structured Regression Models --
2.8 Classes of Restricted Estimators --
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 --
3.3 Multiple Regression from Simple Univariate Regression --
3.4 Subset Selection and Coefficient Shrinkage --
3.5 Computational Considerations --
Bibliographic Notes --
Exercises --
4 Linear Methods for Classification --
4.1 I ntroduction --
4.2 Linear Regression of an Indicator Matrix --
4.3 Linear Discriminant Analysis --
4.4 Logistic Regression --
4.5 Separating Hyperplanes --
Bibliographic Notes --
Exercises --
5 Basis Expansions and Regularization --
5.1 Introduction --
5.2 Piecewise Polynomials and Splines --
5.3 Filtering and Feature Extraction --
5.4 Smoothing Splines --
5.5 Automatic Selection of the Smoothing Parameters --
5.6 Nonparametric Logistic Regression --
5.7 Multidimensional Splines --
5.8 Regularization and Reproducing Kernel Hilbert Spaces --
5.9 Wavelet Smoothing --
Bibliographic Notes --
Exercises --
Appendix : Computational Considerations for Splines --
6 Kernel Methods --
6.1 One-Dimensional Kernel Smoothers --
6.2 Selecting the Width of the Kernel --
6.3 Local Regression in IRp --
6.4 Structured Local Regression Models in IRp --
6.5 Local Likelihood and Other Models --
6.6 Kernel Density Estimation and Classification --
6.7 Radial Basis Functions and Kernels --
6.8 Mixture Models for Density Estimation and Classification --
6.9 Computational Considerations --
Bibliographic Notes --
Exercises --
7 Model Assessment and Selection --
7.1 Introduction --
7.2 Bias, Variance and Model Complexity --
7.3 The Bias ... Variance Decomposition --
7.4 Optimism of the Training Error Rate --
7.5 Estimates of In-Sample Prediction Error --
7.6 The Effective Number of Parameters --
7.7 The Bayesian Approach and BIC --
7.8 Minimum Description Length --
7.9 Vapnik ... Chernovenkis Dimension --
7.10 Cross-Validation --
7.11 BootstrapMethods --
Bibliographic Notes --
Exercises --
8 Model Inference and Averaging --
8.1 Introduction --
8.2 The Bootstrap and Maximum Likelihood Methods --
8.3 Bayesian Methods --
8.4 Relationship Between the Bootstrap and Bayesian Inference --
8.5 The EM Algorithm --
8.6 MCMC for Sampling from the Posterior --
8.7 Bagging --
8.8 Model Averaging and Stacking --
8.9 Stochastic Search : Bumping --
Bibliographic Notes --
Exercises --
9 Additive Models, Trees, and Related Methods --
9.1 Generalized Additive Models --
9.2 Tree-Based Methods --
9.3 PRI MBump Hunting --
9.4 MARS : Multivariate Adaptive Regression Spli.
Series Title: Springer series in statistics.
Responsibility: Trevor Hastie, Robert Tibshirani, Jerome Friedman.

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