An Introduction to Support Vector Machines and Other Kernelbased Learning MethodsThis is the first comprehensive introduction to Support Vector Machines (SVMs), a new generation learning system based on recent advances in statistical learning theory. SVMs deliver stateoftheart performance in realworld applications such as text categorisation, handwritten character recognition, image classification, biosequences analysis, etc., and are now established as one of the standard tools for machine learning and data mining. Students will find the book both stimulating and accessible, while practitioners will be guided smoothly through the material required for a good grasp of the theory and its applications. 
What people are saying  Write a review
User ratings
5 stars 
 
4 stars 
 
3 stars 
 
2 stars 
 
1 star 

Review: An Introduction to Support Vector Machines and Other KernelBased Learning Methods
User Review  Joecolelife  GoodreadsI just happened to read the reviews on the book on Support vector machines by Nello Cristianini and John ShaweTaylor. Could not resist adding my own comments about the book. Excellent book. I plan to ... Read full review
Review: An Introduction to Support Vector Machines and Other KernelBased Learning Methods
User Review  GoodreadsI just happened to read the reviews on the book on Support vector machines by Nello Cristianini and John ShaweTaylor. Could not resist adding my own comments about the book. Excellent book. I plan to ... Read full review
Contents
The Learning Methodology  1 
Linear Learning Machines  9 
KernelInduced Feature Spaces  26 
Generalisation Theory  52 
Optimisation Theory  79 
Support Vector Machines  93 
Implementation Techniques  125 
Applications of Support Vector Machines  149 
A Pseudocode for the SMO Algorithm  162 
173  
187  
Common terms and phrases
1norm soft margin algorithm analysis applied approach Bayesian bias bound Chapter choice classification computational consider constraints convergence convex corresponding datasets Definition described dual problem dual representation fatshattering dimension feasibility gap feature mapping feature space finite Gaussian processes generalisation error geometric margin given Hence heuristics high dimensional Hilbert space hyperplane hypothesis inequality inner product space input space introduced iterative KarushKuhnTucker kernel function kernel matrix Lagrange multipliers Lagrangian learning algorithm linear functions linear learning machines loss function machine learning margin distribution margin slack vector maximal margin hyperplane maximise minimise norm objective function obtained online optimisation problem parameters perceptron perceptron algorithm performance positive semidefinite primal and dual quantity random examples realvalued function Remark result ridge regression Section sequence slack variables soft margin optimisation solution solve subset Support Vector Machines SVMs techniques Theorem training data training examples training points training set update Vapnik VC dimension weight vector zero