Gaussian processes for machine learning /

"Gaussian processes (GPs) provide a principled, practical, probabilistic approach to learning in kernel machines. GPs have received increased attention in the machine-learning community over the past decade, and this book provides a long-needed systematic and unified treatment of theoretical an...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Rasmussen, Carl Edward
Otros Autores: Williams, Christopher K. I.
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Cambridge, Mass. : MIT Press, ©2006.
Colección:Adaptive computation and machine learning.
Temas:
Gaussian processes > Data processing.
Machine learning > Mathematical models.
Processus gaussiens > Informatique.
Apprentissage automatique > Modèles mathématiques.
MATHEMATICS > Probability & Statistics > Stochastic Processes.
Gaussian processes > Data processing
Machine learning > Mathematical models
Gauß-Prozess
Maschinelles Lernen
COMPUTER SCIENCE/Machine Learning & Neural Networks
neurale netwerken
neural networks
statistiek
statistics
gegevensverwerking
data processing
patroonherkenning
pattern recognition
kunstmatige intelligentie
artificial intelligence
Information and Communication Technology (General)
Informatie- en communicatietechnologie (algemeen)
Acceso en línea:Texto completo
Tabla de Contenidos:
  • Regression
  • Classification
  • Covariance functions
  • Model selection and adaptation of hyperparameters
  • Relationships between GPs and other models
  • Theoretical perspectives
  • Approximation methods for large datasets
  • Appendix A : Mathematical background
  • Appendix B : Guassian Markov processes.

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