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This book is a collection of invited contributions from leading researchers in machine learning. Comprised of 21 chapters, this comprehensive reference covers the latest research and advances in regularization, sparsity, and compressed sensing; describes recent progress in convex and large-scale optimization, kernel methods, and support vector machines; and discusses output kernel learning, domain adaptation, multi-layer support vector machines, and more.
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