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Optimization for machine learning /
An up-to-date account of the interplay between optimization and machine learning, accessible to students and researchers in both communities. The interplay between optimization and machine learning is one of the most important developments in modern computational science. Optimization formulations a...
| Clasificación: | Libro Electrónico |
|---|---|
| Otros Autores: | , , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Cambridge, Mass. :
MIT Press,
[2012]
|
| Colección: | Neural information processing series.
|
| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 245 | 0 | 0 | |a Optimization for machine learning / |c edited by Suvrit Sra, Sebastian Nowozin, and Stephen J. Wright. |
| 264 | 1 | |a Cambridge, Mass. : |b MIT Press, |c [2012] | |
| 264 | 4 | |c ©2012 | |
| 300 | |a 1 online resource (ix, 494 pages) : |b illustrations | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
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| 347 | |a data file |2 rda | ||
| 380 | |a Bibliography | ||
| 490 | 1 | |a Neural information processing series | |
| 504 | |a Includes bibliographical references. | ||
| 505 | 0 | |a Introduction : Optimization and machine learning / S. Sra, S. Nowozin, and S.J. Wright -- Convex optimization with sparsity-inducing norms / F. Bach, R. Jenatton, J. Mairal, and G. Obozinski -- Interior-point methods for large-scale cone programming / M. Andersen, J. Dahl, Z. Liu, and L. Vanderberghe -- Incremental gradient, subgradient, and proximal methods for convex optimization : a survey / D.P. Bertsekas -- First-order methods for nonsmooth convex large-scale optimization, I : general purpose methods / A. Juditsky and A. Nemirovski -- First-order methods for nonsmooth convex large-scale optimization, II : utilizing problem's structure / A. Juditsky and A. Nemirovski -- Cutting-plane methods in machine learning / V. Franc, S. Sonnenburg, and T. Werner -- Introduction to dual decomposition for inference / D. Sontag, A. Globerson, and T. Jaakkola -- Augmented Lagrangian methods for learning, selecting, and combining features / R. Tomioka, T. Suzuki, and M. Sugiyama -- The convex optimization approach to regret minimization / E. Hazan -- Projected Newton-type methods in machine learning / M. Schmidt, D. Kim, and S. Sra -- Interior-point methods in machine learning / J. Gondzio -- The tradeoffs of large-scale learning / L. Bottou and O. Bousquet -- Robust optimization in machine learning / C. Caramanis, S. Mannor, and H. Xu -- Improving first and second-order methods by modeling uncertainty / N. Le Roux, Y. Bengio, and A. Fitzgibbon -- Bandit view on noisy optimization / J.-Y. Audibert, S. Bubeck, and R. Munos -- Optimization methods for sparse inverse covariance selection / K. Scheinberg and S. Ma -- A pathwise algorithm for covariance selection / V. Krishnamurthy, S.D. Ahipasaoglu, and A. d'Aspremont. | |
| 588 | 0 | |a Print version record. | |
| 520 | |a An up-to-date account of the interplay between optimization and machine learning, accessible to students and researchers in both communities. The interplay between optimization and machine learning is one of the most important developments in modern computational science. Optimization formulations and methods are proving to be vital in designing algorithms to extract essential knowledge from huge volumes of data. Machine learning, however, is not simply a consumer of optimization technology but a rapidly evolving field that is itself generating new optimization ideas. This book captures the state of the art of the interaction between optimization and machine learning in a way that is accessible to researchers in both fields. Optimization approaches have enjoyed prominence in machine learning because of their wide applicability and attractive theoretical properties. The increasing complexity, size, and variety of today's machine learning models call for the reassessment of existing assumptions. This book starts the process of reassessment. It describes the resurgence in novel contexts of established frameworks such as first-order methods, stochastic approximations, convex relaxations, interior-point methods, and proximal methods. It also devotes attention to newer themes such as regularized optimization, robust optimization, gradient and subgradient methods, splitting techniques, and second-order methods. Many of these techniques draw inspiration from other fields, including operations research, theoretical computer science, and subfields of optimization. The book will enrich the ongoing cross-fertilization between the machine learning community and these other fields, and within the broader optimization community. | ||
| 590 | |a ProQuest Ebook Central |b Ebook Central Academic Complete | ||
| 650 | 0 | |a Machine learning |x Mathematical models. | |
| 650 | 0 | |a Mathematical optimization. | |
| 650 | 6 | |a Apprentissage automatique |x Modèles mathématiques. | |
| 650 | 6 | |a Optimisation mathématique. | |
| 650 | 7 | |a COMPUTERS |x Enterprise Applications |x Business Intelligence Tools. |2 bisacsh | |
| 650 | 7 | |a COMPUTERS |x Intelligence (AI) & Semantics. |2 bisacsh | |
| 650 | 7 | |a COMPUTERS |x Machine Theory. |2 bisacsh | |
| 650 | 7 | |a Machine learning |x Mathematical models |2 fast | |
| 650 | 7 | |a Mathematical optimization |2 fast | |
| 653 | |a COMPUTER SCIENCE/Machine Learning & Neural Networks | ||
| 653 | |a COMPUTER SCIENCE/Artificial Intelligence | ||
| 700 | 1 | |a Sra, Suvrit, |d 1976- | |
| 700 | 1 | |a Nowozin, Sebastian, |d 1980- | |
| 700 | 1 | |a Wright, Stephen J., |d 1960- | |
| 758 | |i has work: |a Optimization for machine learning (Text) |1 https://id.oclc.org/worldcat/entity/E39PCFFBBMytvQHBTPvFTJVq6X |4 https://id.oclc.org/worldcat/ontology/hasWork | ||
| 776 | 0 | 8 | |i Print version: |t Optimization for machine learning. |d Cambridge, Mass. : MIT Press, ©2012 |z 9780262016469 |w (DLC) 2011002059 |w (OCoLC)701493361 |
| 830 | 0 | |a Neural information processing series. | |
| 856 | 4 | 0 | |u https://ebookcentral.uam.elogim.com/lib/uam-ebooks/detail.action?docID=3339310 |z Texto completo |
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