An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods by Cristianini, Nello -- Read -- Imperial Library of Trantor

Index

Cover Table of Contents BackCover An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods Preface Chapter 1: The Learning Methodology

1.1 Supervised Learning 1.2 Learning and Generalisation 1.3 Improving Generalisation 1.4 Attractions and Drawbacks of Learning 1.5 Support Vector Machines for Learning 1.6 Exercises 1.7 Further Reading and Advanced Topics

Chapter 2: Linear Learning Machines

2.2 Linear Regression 2.3 Dual Representation of Linear Machines 2.4 Exercises 2.5 Further Reading and Advanced Topics

Chapter 3: Kernel-Induced Feature Spaces

3.1 Learning in Feature Space 3.2 The Implicit Mapping into Feature Space 3.3 Making Kernels 3.4 Working in Feature Space 3.5 Kernels and Gaussian Processes 3.6 Exercises 3.7 Further Reading and Advanced Topics

Chapter 4: Generalisation Theory

4.1 Probably Approximately Correct Learning 4.2 Vapnik Chervonenkis (VC) Theory 4.3 Margin-Based Bounds on Generalisation 4.4 Other Bounds on Generalisation and Luckiness 4.5 Generalisation for Regression 4.6 Bayesian Analysis of Learning 4.7 Exercises 4.8 Further Reading and Advanced Topics

Chapter 5: Optimisation Theory

5.1 Problem Formulation 5.2 Lagrangian Theory 5.3 Duality 5.4 Exercises 5.5 Further Reading and Advanced Topics

Chapter 6: Support Vector Machines

6.2 Support Vector Regression 6.3 Discussion 6.4 Exercises 6.5 Further Reading and Advanced Topics

Chapter 7: Implementation Techniques

7.1 General Issues 7.2 The Naive Solution: Gradient Ascent 7.3 General Techniques and Packages 7.4 Chunking and Decomposition 7.5 Sequential Minimal Optimisation (SMO) 7.6 Techniques for Gaussian Processes 7.7 Exercises 7.8 Further Reading and Advanced Topics

Chapter 8: Applications of Support Vector Machines

8.1 Text Categorisation 8.2 Image Recognition 8.3 Hand-written Digit Recognition 8.4 Bioinformatics 8.5 Further Reading and Advanced Topics

Appendix A: Pseudocode for the SMO Algorithm Appendix B: Background Mathematics

B.2 Inner Product Spaces B.3 Hilbert Spaces B.4 Operators, Eigenvalues and Eigenvectors

References Index

Index_B Index_C Index_D Index_E Index_F Index_G Index_H Index_I-J Index_K Index_L Index_M Index_N Index_O Index_P Index_Q Index_R Index_S Index_T Index_U Index_V Index_W

List of Figures List of Tables List of Examples

← Prev
Back
Next →

← Prev
Back
Next →