Regularization, Optimization, Kernels, and Support Vector Machines Johan Suykens

ISBN: 9781482241396

Published: October 30th 2014

Hardcover

525 pages


Description

Regularization, Optimization, Kernels, and Support Vector Machines  by  Johan Suykens

Regularization, Optimization, Kernels, and Support Vector Machines by Johan Suykens
October 30th 2014 | Hardcover | PDF, EPUB, FB2, DjVu, talking book, mp3, RTF | 525 pages | ISBN: 9781482241396 | 3.70 Mb

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,MoreRegularization, 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.



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