Book: Regularization, Optimization, Kernels, and Support Vector Machines

Marco Signoretto just sent me the following:

Dear Igor,

can you please post this announcement in nuit-blanche? Thank you in advance

regards

Marco

Sure Marco here is the table of content:

NEW BOOK ANNOUNCEMENT:

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

    Join the CompressiveSensing subreddit or the Google+ Community and post there !
Liked this entry ? subscribe to Nuit Blanche's feed, there's more where that came from. You can also subscribe to Nuit Blanche by Email, explore the Big Picture in Compressive Sensing or the Matrix Factorization Jungle and join the conversations on compressive sensing, advanced matrix factorization and calibration issues on Linkedin.