Cargando…
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.
Autores principales: | Rasmussen, Carl Edward, Williams, Christopher K I |
---|---|
Lenguaje: | eng |
Publicado: |
The MIT Press
2006
|
Materias: | |
Acceso en línea: | http://cds.cern.ch/record/2307304 |
Ejemplares similares
-
Unsupervised process monitoring and fault diagnosis with machine learning methods
por: Aldrich, Chris, et al.
Publicado: (2013) -
Applied natural language processing with Python: implementing machine learning and deep learning algorithms for natural language processing
por: Beysolow, Taweh
Publicado: (2018) -
Deep learning for physical scientists: accelerating research with machine learning
por: Pyzer-Knapp, Edward O
Publicado: (2019) -
Python natural language processing: explore NLP with machine learning and deep learning techniques
por: Thanaki, Jalaj
Publicado: (2017) -
Machine learning for OpenCV: a practical introduction to the world of machine learning and image processing using OpenCV and Python
por: Beyeler, Michael
Publicado: (2017)