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Full Description
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 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 descent
Regularization, Optimization, Kernels, and Support Vector Machines is ideal for researchers in machine learning, pattern recognition, data mining, signal processing, statistical learning, and related areas.
Contents
An Equivalence between the Lasso and Support Vector Machines. Regularized Dictionary Learning. Hybrid Conditional Gradient-Smoothing Algorithms with Applications to Sparse and Low Rank Regularization. Nonconvex Proximal Splitting with Computational Errors. Learning Constrained Task Similarities in Graph-Regularized Multi-Task Learning. The Graph-Guided Group Lasso for Genome-Wide Association Studies. On the Convergence Rate of Stochastic Gradient Descent for Strongly Convex Functions. Detecting Ineffective Features for Nonparametric Regression. Quadratic Basis Pursuit. Robust Compressive Sensing. Regularized Robust Portfolio Estimation. The Why and How of Nonnegative Matrix Factorization. Rank Constrained Optimization Problems in Computer Vision. Low-Rank Tensor Denoising and Recovery via Convex Optimization. Learning Sets and Subspaces. Output Kernel Learning Methods. Kernel Based Identification of Systems with Multiple Outputs Using Nuclear Norm Regularization. Kernel Methods for Image Denoising. Single-Source Domain Adaptation with Target and Conditional Shift. Multi-Layer Support Vector Machines. Online Regression with Kernels.
