Predicting structured data /

State-of-the-art algorithms and theory in a novel domain of machine learning, prediction when the output has structure. Machine learning develops intelligent computer systems that are able to generalize from previously seen examples. A new domain of machine learning, in which the prediction must sat...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor Corporativo: Neural Information Processing Systems Foundation
Otros Autores: BakIr, Gökhan
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Cambridge, Mass. : MIT Press, ©2007.
Colección:Neural information processing series.
Temas:
Machine learning.
Computer algorithms.
Kernel functions.
Data structures (Computer science)
Algorithms.
Algorithms
Machine Learning
Apprentissage automatique.
Algorithmes.
Noyaux (Mathématiques)
Structures de données (Informatique)
algorithms.
COMPUTERS > Enterprise Applications > Business Intelligence Tools.
COMPUTERS > Intelligence (AI) & Semantics.
Computer algorithms
Kernel functions
Machine learning
Lernen > (Informatik) > Kernel (Informatik)
Lernen > (Informatik) > Strukturlogik.
Strukturlogik > Lernen (Informatik)
Kernel > (Informatik) > Lernen (Informatik)
COMPUTER SCIENCE/Machine Learning & Neural Networks
Acceso en línea:Texto completo
Tabla de Contenidos:
  • Measuring Similarity with Kernels
  • Discriminative Models
  • Modeling Structure via Graphical Models
  • Joint Kernel Maps / Jason Weston [and others]
  • Support Vector Machine Learning for Interdependent and Structured Output Spaces / Yasemin Altun, Thomas Hofmann, and Ioannis Tsochandiridis
  • Efficient Algorithms for Max-Margin Structured Classification / Juho Rousu [and others]
  • Discriminative Learning of Prediction Suffix Trees with the Perceptron Algorithm / Ofer Dekel, Shai Shalev-Shwartz, and Yoram Singer
  • A General Regression Framework for Learning String-to-String Mappings / Corinna Cortes, Mehryar Mohri, and Jason Weston
  • Learning as Search Optimization / Hal Daume III and Daniel Marcu
  • Energy-Based Models / Yann LeCun [and others]
  • Generalization Bounds and Consistency for Structured Labeling / David McAllester
  • Kernel Conditional Graphical Models / Fernando Perez-Cruz, Zoubin Ghahramani, and Massimiliano Pontil
  • Density Estimation of Structured Outputs in Reproducing Kernel Hilbert Spaces / Yasemin Altun and Alex J. Smola
  • Gaussian Process Belief Propagation / Matthias W. Seeger.

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