Regularization, optimization, kernels, and support vector machines. (2014)
- Record Type:
- Book
- Title:
- Regularization, optimization, kernels, and support vector machines. (2014)
- Main Title:
- Regularization, optimization, kernels, and support vector machines
- Further Information:
- Note: Editors, Johan A.K. Suykens, Marco Signoretto, Andreas Argyriou.
- Editors:
- Suykens, Johan A. K
Signoretto, Marco
Argyriou, Andreas - Contents:
- Contents Preface Contributors An Equivalence between the Lasso and Support Vector Machines; Martin Jaggi Regularized Dictionary Learning; Annalisa Barla, Saverio Salzo, and Alessandro Verri Hybrid Conditional Gradient-Smoothing Algorithms with Applications to Sparse and Low Rank Regularization; Andreas Argyriou, Marco Signoretto, and Johan A.K. Suykens Nonconvex Proximal Splitting with Computational Errors; Suvrit Sra Learning Constrained Task Similarities in Graph-Regularized Multi-Task Learning; Rémi Flamary, Alain Rakotomamonjy, and Gilles Gasso The Graph-Guided Group Lasso for Genome-Wide Association Studies; Zi Wang and Giovanni Montana On the Convergence Rate of Stochastic Gradient Descent for Strongly Convex Functions; Cheng Tang and Claire Monteleoni Detecting Ineffective Features for Nonparametric Regression; Kris De Brabanter, Paola Gloria Ferrario, and László Györfi Quadratic Basis Pursuit; Henrik Ohlsson, Allen Y. Yang, Roy Dong, Michel Verhaegen, and S. Shankar Sastry Robust Compressive Sensing; Esa Ollila, Hyon-Jung Kim, and Visa Koivunen Regularized Robust Portfolio Estimation; Theodoros Evgeniou, Massimiliano Pontil, Diomidis Spinellis, Rafal Swiderski, and Nick Nassuphis The Why and How of Nonnegative Matrix Factorization; Nicolas Gillis Rank Constrained Optimization Problems in Computer Vision; Ivan Markovsky Low-Rank Tensor Denoising and Recovery via Convex Optimization; Ryota Tomioka, Taiji Suzuki, Kohei Hayashi, and Hisashi Kashima Learning Sets andContents Preface Contributors An Equivalence between the Lasso and Support Vector Machines; Martin Jaggi Regularized Dictionary Learning; Annalisa Barla, Saverio Salzo, and Alessandro Verri Hybrid Conditional Gradient-Smoothing Algorithms with Applications to Sparse and Low Rank Regularization; Andreas Argyriou, Marco Signoretto, and Johan A.K. Suykens Nonconvex Proximal Splitting with Computational Errors; Suvrit Sra Learning Constrained Task Similarities in Graph-Regularized Multi-Task Learning; Rémi Flamary, Alain Rakotomamonjy, and Gilles Gasso The Graph-Guided Group Lasso for Genome-Wide Association Studies; Zi Wang and Giovanni Montana On the Convergence Rate of Stochastic Gradient Descent for Strongly Convex Functions; Cheng Tang and Claire Monteleoni Detecting Ineffective Features for Nonparametric Regression; Kris De Brabanter, Paola Gloria Ferrario, and László Györfi Quadratic Basis Pursuit; Henrik Ohlsson, Allen Y. Yang, Roy Dong, Michel Verhaegen, and S. Shankar Sastry Robust Compressive Sensing; Esa Ollila, Hyon-Jung Kim, and Visa Koivunen Regularized Robust Portfolio Estimation; Theodoros Evgeniou, Massimiliano Pontil, Diomidis Spinellis, Rafal Swiderski, and Nick Nassuphis The Why and How of Nonnegative Matrix Factorization; Nicolas Gillis Rank Constrained Optimization Problems in Computer Vision; Ivan Markovsky Low-Rank Tensor Denoising and Recovery via Convex Optimization; Ryota Tomioka, Taiji Suzuki, Kohei Hayashi, and Hisashi Kashima Learning Sets and Subspaces; Alessandro Rudi, Guillermo D. Canas, Ernesto De Vito, and Lorenzo Rosasco Output Kernel Learning Methods; Francesco Dinuzzo, Cheng Soon Ong, and Kenji Fukumizu Kernel Based Identification of Systems with Multiple Outputs Using Nuclear Norm Regularization; Tillmann Falck, Bart De Moor, and Johan A.K. Suykens Kernel Methods for Image Denoising; Pantelis Bouboulis and Sergios Theodoridis Single-Source Domain Adaptation with Target and Conditional Shift; Kun Zhang, Bernhard Schölkopf, Krikamol Muandet, Zhikun Wang, Zhi-Hua Zhou, and Claudio Persello Multi-Layer Support Vector Machines; Marco A. Wiering and Lambert R.B. Schomaker Online Regression with Kernels; Steven Van Vaerenbergh and Ignacio Santamaría Index … (more)
- Edition:
- 1st
- Publisher Details:
- Boca Raton : Chapman & Hall/CRC
- Publication Date:
- 2014
- Extent:
- 1 online resource, illustrations (black and white)
- Subjects:
- 006.31
Support vector machines
Machine learning - Languages:
- English
- ISBNs:
- 9781482241402
- Related ISBNs:
- 9781482241396
- Notes:
- Note: Includes bibliographical references and index.
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- Legal Deposit; Only available on premises controlled by the deposit library and to one user at any one time; The Legal Deposit Libraries (Non-Print Works) Regulations (UK).
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- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library HMNTS - ELD.DS.144335
- Ingest File:
- 02_136.xml