Learning With Kernals by Bernhard Scholkopf eBook Free Download

 

Learning With Kernals by Bernhard Scholkopf eBook Free Download

Learning With Kernals by Bernhard Scholkopf eBook Free Download

Learning With Kernals by Bernhard Scholkopf eBook Free Download

 

Bolster Vector Machines, Regularization, Optimization, and Beyond

Introduction:

In the 1990s, another sort of learning calculation was created, in light of results from factual learning hypothesis: the Support Vector Machine (SVM). This offered ascend to another class of hypothetically exquisite learning machines that utilization a focal idea of SVMs – pieces – for various learning undertakings. Bit machines give a measured structure that can be adjusted to various assignments and areas by the decision of the piece capacity and the base calculation. They are supplanting neural systems in an assortment of fields, including building, data recovery, and bioinformatics.

Learning with Kernels gives a prologue to SVMs and related bit systems. Despite the fact that the book starts with the fundamentals, it likewise incorporates the most recent examination. It gives the greater part of the ideas important to empower a peruser outfitted with some fundamental numerical information to enter the universe of machine learning utilizing hypothetically all around established yet simple to-utilize portion calculations and to comprehend and apply the effective calculations that have been created in the course of the most recent couple of years.

Contents:

1 A Tutorial Introduction 1

1.1 Data Representation and Similarity . . . . . . . . . . . . . . . . . . . 1

1.2 A Simple Pattern Recognition Algorithm . . . . . . . . . . . . . . . 4

1.3 Some Insights From Statistical Learning Theory . . . . . . . . . . . 6

1.4 Hyperplane Classifiers . . . . . . . . . . . . . . . . . . . . . . . . . . 11

I CONCEPTS AND TOOLS 23

2 Kernels 25

2.1 Product Features . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

2.2 The Representation of Similarities in Linear Spaces . . . . . . . . . . 29

2.3 Examples and Properties of Kernels . . . . . . . . . . . . . . . . . . 45

3 Risk and Loss Functions 61

3.1 Loss Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62

3.2 Test Error and Expected Risk . . . . . . . . . . . . . . . . . . . . . . 65

3.3 A Statistical Perspective . . . . . . . . . . . . . . . . . . . . . . . . . 68

3.4 Robust Estimators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75

4 Regularization 87

4.1 The Regularized Risk Functional . . . . . . . . . . . . . . . . . . . . 88

5 Elements of Statistical Learning Theory 125

5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125

5.2 The Law of Large Numbers . . . . . . . . . . . . . . . . . . . . . . . 128

5.3 When Does LearningWork: the Question of Consistency . . . . . . 131

6 Optimization 149

6.1 Convex Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . 150

6.2 Unconstrained Problems . . . . . . . . . . . . . . . . . . . . . . . . . 154

6.3 Constrained Problems . . . . . . . . . . . . . . . . . . . . . . . . . . 165

6.4 Interior Point Methods . . . . . . . . . . . . . . . . . . . . . . . . . . 175

II SUPPORT VECTOR MACHINES 187

7 Pattern Recognition 189

7.1 Separating Hyperplanes . . . . . . . . . . . . . . . . . . . . . . . . . 189

7.2 The Role of the Margin . . . . . . . . . . . . . . . . . . . . . . . . . . 192

7.3 Optimal Margin Hyperplanes . . . . . . . . . . . . . . . . . . . . . . 196

7.4 Nonlinear Support Vector Classifiers . . . . . . . . . . . . . . . . . . 200

8 Single-Class Problems: Quantile Estimation and Novelty Detection 227

8.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 228

8.2 A Distribution’s Support and Quantiles . . . . . . . . . . . . . . . . 229

8.3 Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 230

9 Regression Estimation 251

9.1 Linear Regression with Insensitive Loss Function . . . . . . . . . . . 251

9.2 Dual Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 254

9.3 -SV Regression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 260

 

About the Author:

Bernhard Schölkopf is Professor and Director at the Max Planck Institute for Biological Cybernetics in Tübingen, Germany. He is coauthor of Learning with Kernels (2002) and is a coeditor of Advances in Kernel Methods: Support Vector Learning (1998), Advances in Large-Margin Classifiers (2000), and Kernel Methods in Computational Biology (2004), all distributed by the MIT Press.

Alexander J. Smola is Senior Principal Researcher and Machine Learning Program Leader at National ICT Australia/Australian National University, Canberr.

Learning With Kernals by Bernhard Scholkopf eBook Free Download

 

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