Regularization optimization kernels and support vector machines 
/ edited by Johan A K Suykens, Marco Signoretto and Andreas Argyriou  
 - Boca Raton :  CRC Press.    2015, 
 - xvii, 507p.  : ill. 
<br/><br/> Index 503-507p. 
<br/><br/> 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. 
<br/><br/><label>ISBN: </label>9781482241396  
<br/><br/><label>Source: </label> Textbook 
<br/><br/><label>Subjects--Topical Terms: </label><br/> Nonparametric<br/> Subspaces<br/>Nonconvex proximal splitting