By Johan A.K. Suykens,Marco Signoretto,Andreas Argyriou

Regularization, Optimization, Kernels, and help Vector Machines bargains a photograph of the present cutting-edge of large-scale computing device studying, delivering a unmarried multidisciplinary resource for the newest examine and advances in regularization, sparsity, compressed sensing, convex and large-scale optimization, kernel tools, and aid vector machines. inclusive of 21 chapters authored through best researchers in computing device studying, this accomplished reference:



  • Covers the connection among help vector machines (SVMs) and the Lasso

  • Discusses multi-layer SVMs

  • Explores nonparametric characteristic choice, foundation pursuit equipment, and strong compressive sensing

  • Describes graph-based regularization equipment for unmarried- and multi-task learning

  • Considers regularized tools for dictionary studying and portfolio selection

  • Addresses non-negative matrix factorization

  • Examines low-rank matrix and tensor-based models

  • Presents complex kernel tools for batch and on-line computing device studying, procedure identity, area version, and photograph processing

  • Tackles large-scale algorithms together with conditional gradient tools, (non-convex) proximal concepts, and stochastic gradient descent

Regularization, Optimization, Kernels, and help Vector Machines is perfect for researchers in desktop studying, trend attractiveness, info mining, sign processing, statistical studying, and comparable areas.

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Regularization, Optimization, Kernels, and Support Vector Machines (Chapman & Hall/Crc Machine Learning & Pattern Recognition Series) by Johan A.K. Suykens,Marco Signoretto,Andreas Argyriou


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