CRC Press, 2014. — 525 p. — ISBN: 9781482241396, e-ISBN: 9781482241402.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 Lasso
Discusses multi-layer SVMs
Explores nonparametric feature selection, basis pursuit methods, and robust compressive sensing
Describes graph-based regularization methods for single- and multi-task learning
Considers regularized methods for dictionary learning and portfolio selection
Addresses non-negative matrix factorization
Examines low-rank matrix and tensor-based models
Presents advanced kernel methods for batch and online machine learning, system identification, domain adaptation, and image processing
Tackles 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.
