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

chapter 1|26 pages

An Equivalence between the Lasso and Support Vector Machines

ByMartin Jaggi

chapter 2|26 pages

Regularized Dictionary Learning

ByAnnalisa Barla, Saverio Salzo, Alessandro Verri

chapter 3|30 pages

Hybrid Conditional Gradient-Smoothing Algorithms with Applications to Sparse and Low Rank Regularization

ByAndreas Argyriou, Marco Signoretto, and Johan A.K. Suykens

chapter 5|28 pages

Learning Constrained Task Similarities in Graph-Regularized Multi-Task Learning

ByMulti-Task Learning Rémi Flamary, Alain Rakotomamonjy, and Gilles Gasso

chapter 6|28 pages

The Graph-Guided Group Lasso for Genome-Wide Association Studies

ByZi Wang, Giovanni Montana

chapter 7|18 pages

On the Convergence Rate of Stochastic Gradient Descent for Strongly Convex Functions

ByCheng Tang, Claire Monteleoni

chapter 8|18 pages

Detecting Ineffective Features for Nonparametric Regression

ByKris De Brabanter, Paola Gloria Ferrario, László Györfi

chapter 9|22 pages

Quadratic Basis Pursuit

ByHenrik Ohlsson, Allen Y. Yang, Roy Dong, Michel Verhaegen, S. Shankar Sastry

chapter 10|20 pages

Robust Compressive Sensing

ByEsa Ollila, Hyon-Jung Kim, Visa Koivunen

chapter 11|20 pages

Regularized Robust Portfolio Estimation

ByTheodoros Evgeniou, Massimiliano Pontil, Diomidis Spinellis, Nick Nassuphis

chapter 12|36 pages

The Why and How of Nonnegative Matrix Factorization

ByNicolas Gillis

chapter 13|20 pages

Rank Constrained Optimization Problems in Computer Vision

ByIvan Markovsky

chapter 14|24 pages

Low-Rank Tensor Denoising and Recovery via Convex Optimization

ByRyota Tomioka, Taiji Suzuki, Kohei Hayashi, Hisashi Kashima

chapter 15|22 pages

Learning Sets and Subspaces

ByAlessandro Rudi, Guillermo D. Canas, Ernesto De Vito, Lorenzo Rosasco

chapter 16|12 pages

Output Kernel Learning Methods

ByFrancesco Dinuzzo, Cheng Soon Ong, Kenji Fukumizu

chapter 17|30 pages

Kernel Based Identification of Systems with Multiple Outputs Using Nuclear Norm Regularization

ByTillmann Falck, Bart De Moor, and Johan A.K. Suykens

chapter 18|26 pages

Kernel Methods for Image Denoising

ByPantelis Bouboulis, Sergios Theodoridis

chapter 20|20 pages

Multi-Layer Support Vector Machines

ByMarco A. Wiering and Lambert R.B. Schomaker

chapter 21|27 pages

Online Regression with Kernels

BySteven Van Vaerenbergh, Ignacio Santamaría