Regularization Optimization Kernels And Support Vector Machines(1st Edition)
Authors:
Johan A K Suykens ,Marco Signoretto ,Andreas Argyriou
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ISBN: 0367658984, 978-0367658984
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Book Price $0 : 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, This Comprehensive Reference:Covers The Relationship Between Support Vector Machines (SVMs) And The LassoDiscusses Multi-layer SVMsExplores Nonparametric Feature Selection, Basis Pursuit Methods, And Robust Compressive SensingDescribes Graph-based Regularization Methods For Single- And Multi-task LearningConsiders Regularized Methods For Dictionary Learning And Portfolio SelectionAddresses Non-negative Matrix FactorizationExamines Low-rank Matrix And Tensor-based ModelsPresents Advanced Kernel Methods For Batch And Online Machine Learning, System Identification, Domain Adaptation, And Image ProcessingTackles 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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