Regularization, Optimization, Kernels, and Support Vector Machines offers a snapshot of the current state of the art of large-scale machine learning, providing a single multidisciplinary source for the latest research and advances in regularization, sparsity, compressed sensing, convex and large-scale optimization, kernel methods, and support vector machines. Consisting of 21 chapters authored by leading researchers in machine learning, this comprehensive reference:Covers the relationship between support vector machines (SVMs) and the LassoDiscusses multi-layer SVMsExplores nonparametric feature selection, basis pursuit methods, and robust compressive sensingDescribes graph-based regularization methods for single- and multi-task learningConsiders regularized methods for dictionary learning and portfolio selectionAddresses non-negative matrix factorizationExamines low-rank matrix and tensor-based modelsPresents advanced kernel methods for batch and online machine learning, system identification, domain adaptation, and image processingTackles large-scale algorithms including conditional gradient methods, (non-convex) proximal techniques, and stochastic gradient descentRegularization, Optimization, Kernels, and Support Vector Machines is ideal for researchers in machine learning, pattern recognition, data mining, signal processing, statistical learning, and related areas.
