Regularization, optimization, kernels, and support vector machines /

"Obtaining reliable models from given data is becoming increasingly important in a wide range of different applications fields including the prediction of energy consumption, complex networks, environmental modelling, biomedicine, bioinformatics, finance, process modelling, image and signal pro...

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Bibliographic Details
Call Number:Libro Electrónico
Corporate Author: ROKS (Workshop) Leuven, Belgium)
Other Authors: Suykens, Johan A. K. (Editor), Signoretto, Marco (Editor), Argyriou, Andreas (Editor)
Format: Electronic Conference Proceeding eBook
Language:Inglés
Published: Boca Raton : CRC Press, [2015]
Series:Chapman & Hall/CRC machine learning & pattern recognition series.
Subjects:
Mathematical models > Congresses.
Mathematical statistics > Congresses.
Modèles mathématiques > Congrès.
MATHEMATICS > General.
Mathematical models
Mathematical statistics
Electronic books.
proceedings (reports)
Conference papers and proceedings
Conference papers and proceedings.
Actes de congrès.
Online Access:Texto completo
Table of Contents:
  • 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 / Rémi 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 László Györfi
  • 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, 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 Schölkopf, 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 Santamaría.

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