{"product_id":"regularization-optimization-kernels-and-support-vector-machines-9780367658984","title":"Regularization, Optimization, Kernels, and Support Vector Machines","description":"\u003cp\u003e • Author(s): Johan A.K. Suykens\u003cbr\u003e • Publisher: Taylor \u0026amp; Francis\u003cbr\u003e • Publisher Imprint: CRC Press\u003cbr\u003e • BISAC: Programming - Games\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eRegularization, Optimization, Kernels, and Support Vector Machines\u003c\/strong\u003e 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: \u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cul\u003e \u003cp\u003e\u003c\/p\u003e\n\u003cli\u003eCovers the relationship between support vector machines (SVMs) and the Lasso\u003c\/li\u003e \u003cp\u003e\u003c\/p\u003e\n\u003cli\u003eDiscusses multi-layer SVMs\u003c\/li\u003e \u003cp\u003e\u003c\/p\u003e\n\u003cli\u003eExplores nonparametric feature selection, basis pursuit methods, and robust compressive sensing\u003c\/li\u003e \u003cp\u003e\u003c\/p\u003e\n\u003cli\u003eDescribes graph-based regularization methods for single- and multi-task learning\u003c\/li\u003e \u003cp\u003e\u003c\/p\u003e\n\u003cli\u003eConsiders regularized methods for dictionary learning and portfolio selection\u003c\/li\u003e \u003cp\u003e\u003c\/p\u003e\n\u003cli\u003eAddresses non-negative matrix factorization\u003c\/li\u003e \u003cp\u003e\u003c\/p\u003e\n\u003cli\u003eExamines low-rank matrix and tensor-based models\u003c\/li\u003e \u003cp\u003e\u003c\/p\u003e\n\u003cli\u003ePresents advanced kernel methods for batch and online machine learning, system identification, domain adaptation, and image processing\u003c\/li\u003e \u003cp\u003e\u003c\/p\u003e\n\u003cli\u003eTackles large-scale algorithms including conditional gradient methods, (non-convex) proximal techniques, and stochastic gradient descent\u003c\/li\u003e \u003c\/ul\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cb\u003eRegularization, Optimization, Kernels, and Support Vector Machines\u003c\/b\u003e is ideal for researchers in machine learning, pattern recognition, data mining, signal processing, statistical learning, and related areas.\u003c\/p\u003e","brand":"Taylor \u0026 Francis","offers":[{"title":"Paperback","offer_id":45240575787159,"sku":"9780367658984","price":4071.0,"currency_code":"INR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0666\/3471\/1191\/files\/9780367658984.webp?v=1769226009","url":"https:\/\/atlanticbooks.com\/products\/regularization-optimization-kernels-and-support-vector-machines-9780367658984","provider":"Atlantic Books","version":"1.0","type":"link"}