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...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Otros Autores: Sra, Suvrit, 1976-, Nowozin, Sebastian, 1980-, Wright, Stephen J., 1960-
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Cambridge, Mass. : MIT Press, [2012]
Colección:Neural information processing series.
Temas:
Machine learning > Mathematical models.
Mathematical optimization.
Apprentissage automatique > Modèles mathématiques.
Optimisation mathématique.
COMPUTERS > Enterprise Applications > Business Intelligence Tools.
COMPUTERS > Intelligence (AI) & Semantics.
COMPUTERS > Machine Theory.
Machine learning > Mathematical models
Mathematical optimization
COMPUTER SCIENCE/Machine Learning & Neural Networks
COMPUTER SCIENCE/Artificial Intelligence
Acceso en línea:Texto completo
Tabla de Contenidos:
  • 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.

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