Learning with Kernels: Support Vector Machines, Regularization, Optimization, and BeyondA comprehensive introduction to Support Vector Machines and related kernel methods. In the 1990s, a new type of learning algorithm was developed, based on results from statistical learning theory: the Support Vector Machine (SVM). This gave rise to a new class of theoretically elegant learning machines that use a central concept of SVMs  kernelsfor a number of learning tasks. Kernel machines provide a modular framework that can be adapted to different tasks and domains by the choice of the kernel function and the base algorithm. They are replacing neural networks in a variety of fields, including engineering, information retrieval, and bioinformatics. Learning with Kernels provides an introduction to SVMs and related kernel methods. Although the book begins with the basics, it also includes the latest research. It provides all of the concepts necessary to enable a reader equipped with some basic mathematical knowledge to enter the world of machine learning using theoretically wellfounded yet easytouse kernel algorithms and to understand and apply the powerful algorithms that have been developed over the last few years. 
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Once you master this book, no doubt you will be an expert in kernelbased learning methods. From my experience, those readers with no math background need a strong patience to consume the equations explained.
Also note, that reading and understanding the book without solving the problems at the end of each chapter is not the best way to learn. Solve every problem.
My regards to the authors.
You can't really understand modern supervised machine learning until you've mastered the techniques in this book.
Contents
A Tutorial Introduction  1 
CONCEPTS AND TOOLS  23 
Risk and Loss Functions  61 
Regularization  87 
Elements of Statistical Learning Theory  125 
Optimization  149 
SUPPORT VECTOR MACHINES  187 
Quantile Estimation and Novelty Detection  227 
KERNEL METHODS  405 
Kernel Feature Extraction  427 
Kernel Fisher Discriminant  457 
Bayesian Kernel Methods  469 
Regularized Principal Manifolds  517 
PreImages and Reduced Set Methods  543 
A Addenda  569 
B Mathematical Prerequisites  575 
Regression Estimation  251 
Implementation  279 
Incorporating Invariances  333 
Learning Theory Revisited  359 
591  
617  
Notation and Symbols  625 