Gaussian processes for machine learning

Gaussian processes for machine learning

A comprehensive and self-contained introduction to Gaussian processes, which provide a principled, practical, probabilistic approach to learning in kernel machines.

Detalles Bibliográficos
Autor principal: Rasmussen, Carl Edward (-)
Otros Autores: Williams, Christopher K. I.
Formato: Libro electrónico
Idioma:Inglés
Publicado: Cambridge, Mass. : MIT Press c2006.
Edición:1st ed
Colección:Adaptive computation and machine learning.
Materias:
Gaussian processes > Data processing.
Machine learning > Mathematical models.
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009746186906719
Tabla de Contenidos:
  • Intro
  • Series Foreword
  • Preface
  • Symbols and Notation
  • Chapter 1 Introduction
  • Chapter 2 Regression
  • Chapter 3 Classification
  • Chapter 4 Covariance functions
  • Chapter 5 Model Selection and Adaptation of Hyperparameters
  • Chapter 6 Relationships between GPs and Other Models
  • Chapter 7 Theoretical Perspectives
  • Chapter 8 Approximation Methods for Large Datasets
  • Chapter 9 Further Issues and Conclusions
  • Appendix A Mathematical Background
  • Appendix B Gaussian Markov Processes
  • Appendix C Datasets and Code
  • Bibliography
  • Author Index
  • Subject Index.

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