Andreas Argyriou – författare

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.

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 vecto

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.