Regularization, Optimization, Kernels, and Support Vector Machines

Johan A.K. Suykens, Marco Signoretto, Andreas Argyriou

• Publisher: Chapman and Hall/CRC
• Year: 2014
• eBook ISBN: 9781040056936
• Topic: Computer Science
• Subtopic: Computer Science General
• Index: Computer Science

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

Table of contents

• Cover
• Half-Title Page
• Title Page
• Copyright Page
• Table of Contents
• Preface
• Contributors
• 1 An Equivalence between the Lasso and Support Vector Machines
• 2 Regularized Dictionary Learning
• 3 Hybrid Conditional Gradient-Smoothing Algorithms with Applications to Sparse and Low Rank Regularization
• 4 Nonconvex Proximal Splitting with Computational Errors
• 5 Learning Constrained Task Similarities in Graph-Regularized Multi-Task Learning
• 6 The Graph-Guided Group Lasso for Genome-Wide Association Studies
• 7 On the Convergence Rate of Stochastic Gradient Descent for Strongly Convex Functions
• 8 Detecting Ineffective Features for Nonparametric Regression
• 9 Quadratic Basis Pursuit
• 10 Robust Compressive Sensing
• 11 Regularized Robust Portfolio Estimation
• 12 The Why and How of Nonnegative Matrix Factorization
• 13 Rank Constrained Optimization Problems in Computer Vision
• 14 Low-Rank Tensor Denoising and Recovery via Convex Optimization
• 15 Learning Sets and Subspaces
• 16 Output Kernel Learning Methods
• 17 Kernel-Based Identifìcation of Systems with Multiple Outputs Using Nuclear Norm Regularization
• 18 Kernel Methods for Image Denoising
• 19 Single-Source Domain Adaptation with Target and Conditional Shift
• 20 Multi-Layer Support Vector Machines
• 21 Online Regression with Kernels
• Index