Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
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 — -kernels—for 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...
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A comprehensive introduction to Support Vector Machines and related kernel methods.
Series ForewordPreface1An Tutorial Introduction1IConcepts and Tools232Kernels253Risk and Loss Functions614Regularization875Elements of Statistical Learning Theory1256Optimization149IISupport Vector Machines1877Pattern Recognition1898Single-Class Problems: Qantile Estimation and Novelty Detection2279Regression Estimation25110Implementation27911Incorporating Invariances33312Learning Theory Revisited359IIIKernel Methods40513Designing Kernels40714Kernel Feature Extraction42715Kernel Fisher Discriminant45716Bayesian Kernel Methods46917Regularized Principal Manifolds51718Pre-Images and Reduced Set Methods543A: Addenda569BMathematical Prerequisites575References591Index617Notation and Symbols625
