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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...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor Corporativo: | |
| Otros Autores: | , , |
| Formato: | Electrónico Congresos, conferencias eBook |
| Idioma: | Inglés |
| Publicado: |
Boca Raton :
CRC Press,
[2015]
|
| Colección: | Chapman & Hall/CRC machine learning & pattern recognition series.
|
| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 111 | 2 | |a ROKS (Workshop) |d (2013 : |c Leuven, Belgium), |j author. | |
| 245 | 1 | 0 | |a Regularization, optimization, kernels, and support vector machines / |c edited by Johan A.K. Suykens, Marco Signoretto, Andreas Argyriou. |
| 264 | 1 | |a Boca Raton : |b CRC Press, |c [2015] | |
| 264 | 4 | |c ©2015 | |
| 300 | |a 1 online resource (xvii, 522 pages) : |b illustrations | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 490 | 1 | |a Chapman & Hall/CRC machine learning & pattern recognition series | |
| 500 | |a "A Chapman and Hall book." | ||
| 504 | |a Includes bibliographical references and index. | ||
| 520 | |a "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 processing, brain-computer interfaces, and others. In data-driven modelling approaches one has witnessed considerable progress in the understanding of estimating flexible nonlinear models, learning and generalization aspects, optimization methods, and structured modelling. One area of high impact both in theory and applications is kernel methods and support vector machines. Optimization problems, learning, and representations of models are key ingredients in these methods. On the other hand, considerable progress has also been made on regularization of parametric models, including methods for compressed sensing and sparsity, where convex optimization plays an important role. At the international workshop ROKS 2013 Leuven, 1 July 8-10, 2013, researchers from diverse fields were meeting on the theory and applications of regularization, optimization, kernels, and support vector machines. At this occasion the present book has been edited as a follow-up to this event, with a variety of invited contributions from presenters and scientific committee members. It is a collection of recent progress and advanced contributions on these topics, addressing methods including ..."-- |c Provided by publisher | ||
| 505 | 0 | 0 | |g 1. |t An equivalence between the lasso and support vector machines / |r Martin Jaggi -- |g 2. |t Regularized dictionary learning / |r Annalisa Barla, Saverio Salzo, and Alessandro Verri -- |g 3. |t Hybrid conditional gradient-smoothing algorithms with applications to sparse and low rank regularization / |r Andreas Argyriou, Marco Signoretto, and Johan A.K. Suykens -- |g 4. |t Nonconvex proximal splitting with computational errors / |r Suvrit Sra -- |g 5. |t Learning constrained task similarities in graph-regularized multi-task learning / |r Rémi Flamary, Alain Rakotomamonjy, and Gilles Gasso -- |g 6. |t The graph-guided group lasso for genome-wide association studies / |r Zi Wang and Giovanni Montana -- |g 7. |t On the convergence rate of stochastic gradient descent for strongly convex functions / |r Cheng Tang and Claire Monteleoni -- |g 8. |t Detecting ineffective features for nonparametric regression / |r Kris De Brabanter, Paola Gloria Ferrario, and László Györfi -- |g 9. |t Quadratic basis pursuit / |r Henrik Ohlsson, Allen Y. Yang, Roy Dong, Michel Verhaegen, and S. Shankar Sastry -- |g 10. |t Robust compressive sensing / |r Esa Ollila, Hyon-Jung Kim, and Visa Koivunen -- |g 11. |t Regularized robust portfolio estimation / |r Theodoros Evgeniou, Massimiliano Pontil, Diomidis Spinellis, and Nick Nassuphis -- |g 12. |t The why and how of nonnegative matrix factorization / |r Nicolas Gillis -- |g 13. |t Rank constrained optimization problems in computer vision / |r Ivan Markovsky -- |g 14. |t Low-rank tensor denoising and recovery via convex optimization / |r Ryota Tomioka, Taiji Suzuki, Kohei Hayashi, and Hisashi Kashima -- |g 15. |t Learning sets and subspaces / |r Alessandro Rudi, Guillermo D. Canas, Ernesto De Vito, and Lorenzo Rosasco -- |g 16. |t Output kernel learning methods / |r Francesco Dinuzzo, Cheng Soon Ong, and Kenji Fukumizu -- |g 17. |t Kernel based identification of systems with multiple outputs using nuclear norm regularization / |r Tillmann Falck, Bart De Moor, and Johan A.K. Suykens -- |g 18. |t Kernel methods for image denoising / |r Pantelis Bouboulis and Sergios Theodoridis -- |g 19. |t Single-source domain adaptation with target and conditional shift / |r Kun Zhang, Bernhard Schölkopf, Krikamol Muandet, Zhikun Wang, Zhi-Hua Zhou, and Claudio Persello -- |g 20. |t Multi-layer support vector machines / |r Marco A. Wiering and Lambert R.B. Schomaker -- |g 21. |t Online regression with kernels / |r Steven Van Vaerenbergh and Ignacio Santamaría. |
| 588 | 0 | |a Print version record. | |
| 546 | |a English. | ||
| 590 | |a ProQuest Ebook Central |b Ebook Central Academic Complete | ||
| 650 | 0 | |a Mathematical models |v Congresses. | |
| 650 | 0 | |a Mathematical statistics |v Congresses. | |
| 650 | 6 | |a Modèles mathématiques |v Congrès. | |
| 650 | 7 | |a MATHEMATICS |x General. |2 bisacsh | |
| 650 | 7 | |a Mathematical models |2 fast | |
| 650 | 7 | |a Mathematical statistics |2 fast | |
| 655 | 0 | |a Electronic books. | |
| 655 | 7 | |a proceedings (reports) |2 aat | |
| 655 | 7 | |a Conference papers and proceedings |2 fast | |
| 655 | 7 | |a Conference papers and proceedings. |2 lcgft | |
| 655 | 7 | |a Actes de congrès. |2 rvmgf | |
| 700 | 1 | |a Suykens, Johan A. K., |e editor. | |
| 700 | 1 | |a Signoretto, Marco, |e editor. | |
| 700 | 1 | |a Argyriou, Andreas, |e editor. | |
| 758 | |i has work: |a Regularization, optimization, kernels, and support vector machines (Text) |1 https://id.oclc.org/worldcat/entity/E39PCFWWQkyX6hXTcvK3dvqWjC |4 https://id.oclc.org/worldcat/ontology/hasWork | ||
| 776 | 0 | 8 | |i Print version: |a ROKS (Workshop) (2013 : Leuven, Belgium). |t Regularization, optimization, kernels, and support vector machines. |d Boca Raton : CRC Press, [2015] |z 9781482241396 |w (DLC) 2014034076 |w (OCoLC)890912544 |
| 830 | 0 | |a Chapman & Hall/CRC machine learning & pattern recognition series. | |
| 856 | 4 | 0 | |u https://ebookcentral.uam.elogim.com/lib/uam-ebooks/detail.action?docID=1674068 |z Texto completo |
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