Contents -- Series Foreword -- Preface -- 1 - A Tutorial Introduction -- I - Concepts and Tools -- 2 - Kernels -- 3 - Risk and Loss Functions -- 4 - Regularization -- 5 - Elements of Statistical Learning Theory -- 6 - Optimization -- II - Support Vector Machines -- 7 - Pattern Recognition -- 8 - Single-Class Problems: Quantile Estimation and Novelty Detection -- 9 - Regression Estimation -- 10 - Implementation -- 11 - Incorporating Invariances -- 12 - Learning Theory Revisited -- III - Kernel Methods -- 13 - Designing Kernels -- 14 - Kernel Feature Extraction -- 15 - Kernel Fisher Discriminant -- 16 - Bayesian Kernel Methods -- 17 - Regularized Principal Manifolds -- 18 - Pre-Images and Reduced Set Methods -- A - Addenda -- B - Mathematical Prerequisites -- References -- Index -- Notation and Symbols.