NEW BOOK: Regularization, Optimization, Kernels, and Support Vector Machines

NEW BOOK: Regularization, Optimization, Kernels, and Support Vector Machines

Posted by Rebecca Martin on Tue, 25/11/2014 - 16:44

Regularization, Optimization, Kernels, and Support Vector Machines
Editors: Johan A.K. Suykens, Marco Signoretto, Andreas Argyriou Chapman and Hall/CRC, Oct 2014

http://www.crcpress.com/product/isbn/9781482241396

Chapter contributions:

1. An Equivalence between the Lasso and Support Vector Machines; Martin Jaggi 2. Regularized Dictionary Learning; Annalisa Barla, Saverio Salzo, and Alessandro Verri 3. Hybrid Conditional Gradient-Smoothing Algorithms with Applications to Sparse and Low Rank Regularization; Andreas Argyriou, Marco Signoretto, and Johan A.K. Suykens 4. Nonconvex Proximal Splitting with Computational Errors; Suvrit Sra 5. Learning Constrained Task Similarities in Graph-Regularized Multi-Task Learning; Remi Flamary, Alain Rakotomamonjy, and Gilles Gasso 6. The Graph-Guided Group Lasso for Genome-Wide Association Studies; Zi Wang and Giovanni Montana 7. On the Convergence Rate of Stochastic Gradient Descent for Strongly Convex Functions; Cheng Tang and Claire Monteleoni 8. Detecting Ineffective Features for Nonparametric Regression; Kris De Brabanter, Paola Gloria Ferrario, and Laszlo Gyorfi 9. Quadratic Basis Pursuit; Henrik Ohlsson, Allen Y. Yang, Roy Dong, Michel Verhaegen, and S. Shankar Sastry 10. Robust Compressive Sensing; Esa Ollila, Hyon-Jung Kim, and Visa Koivunen 11. Regularized Robust Portfolio Estimation; Theodoros Evgeniou, Massimiliano Pontil, Diomidis Spinellis, Rafal Swiderski, and Nick Nassuphis 12. The Why and How of Nonnegative Matrix Factorization; Nicolas Gillis 13. Rank Constrained Optimization Problems in Computer Vision; Ivan Markovsky 14. Low-Rank Tensor Denoising and Recovery via Convex Optimization; Ryota Tomioka, Taiji Suzuki, Kohei Hayashi, and Hisashi Kashima 15. Learning Sets and Subspaces; Alessandro Rudi, Guillermo D. Canas, Ernesto De Vito, and Lorenzo Rosasco 16. Output Kernel Learning Methods; Francesco Dinuzzo, Cheng Soon Ong, and Kenji Fukumizu 17. Kernel Based Identification of Systems with Multiple Outputs Using Nuclear Norm Regularization; Tillmann Falck, Bart De Moor, and Johan A.K. Suykens 18. Kernel Methods for Image Denoising; Pantelis Bouboulis and Sergios Theodoridis 19. Single-Source Domain Adaptation with Target and Conditional Shift; Kun Zhang, Bernhard Scholkopf, Krikamol Muandet, Zhikun Wang, Zhi-Hua Zhou, and Claudio Persello 20. Multi-Layer Support Vector Machines; Marco A. Wiering and Lambert R.B. Schomaker 21. Online Regression with Kernels; Steven Van Vaerenbergh and Ignacio Santamaria