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Principles and Theory for Data Mining and Machine Learning (Springer Series in Statistics) by Bertrand Clarke, Ernest Fokoue, Hao Helen Zhang (auth.) is a nonfiction available to read on EtoBox.

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Preface 1 Variability, Information, and Prediction 16 The Curse of Dimensionality 18 The Two Extremes 19 Perspectives on the Curse 20 Sparsity 21 Exploding Numbers of Models 23 Multicollinearity and Concurvity 24 The Effect of Noise 25 Coping with the Curse 26 Selecting Design Points 26 Local Dimension 27 Parsimony 32 Two Techniques 33 The Bootstrap 33 Cross-Validation 42 Optimization and Search 47 Univariate Search 47 Multivariate Search 48 General Searches 49 Constraint Satisfaction and Combinatorial Search 50 Notes 53 Hammersley Points 53 Edgeworth Expansions for the Mean 54 Bootstrap Asymptotics for the Studentized Mean 56 Exercises 58 Local Smoothers 68 Early Smoothers 70 Transition to Classical Smoothers 74 Global Versus Local Approximations 75 LOESS 79 Kernel Smoothers 82 Statistical Function Approximation 83 The Concept of Kernel Methods and the Discrete Case 88 Kernels and Stochastic Designs: Density Estimation 93 Stochastic Designs: Asymptotics for Kernel Smoothers 96 Convergence Theorems and Rates for Kernel Smoothers 101 Kernel and Bandwidth Selection 105 Linear Smoothers 110 Nearest Neighbors 111 Applications of Kernel Regression 115 A Simulated Example 115 Ethanol D

Who reads Principles and Theory for Data Mining and Machine Learning (Springer Series in Statistics)?

Author

Bertrand Clarke, Ernest Fokoue, Hao Helen Zhang (auth.)

Publisher

Springer-Verlag New York

Published

2009

Language

EN

ISBN

9780387981352

Category

nonfiction

Subjects

Mathematics, Computer Science, Science