Gaussian Processes for Machine Learning
by Carl E. Rasmussen, Christopher K. I. Williams
Publisher: The MIT Press 2005
ISBN/ASIN: 026218253X
ISBN-13: 9780262182539
Number of pages: 266
Description:
Gaussian processes (GPs) provide a principled, practical, probabilistic approach to learning in kernel machines. The book deals with the supervised-learning problem for both regression and classification, and includes detailed algorithms. A wide variety of covariance (kernel) functions are presented and their properties discussed. Model selection is discussed both from a Bayesian and a classical perspective. Many connections to other well-known techniques from machine learning and statistics are discussed, including support-vector machines, neural networks, splines, regularization networks, relevance vector machines and others.
Download or read it online for free here:
Download link
(multiple PDF files)
Similar books
Bayesian Reasoning and Machine Learningby David Barber - Cambridge University Press
The book is designed for final-year undergraduate students with limited background in linear algebra and calculus. Comprehensive and coherent, it develops everything from basics to advanced techniques within the framework of graphical models.
(18757 views)
The Elements of Statistical Learning: Data Mining, Inference, and Predictionby T. Hastie, R. Tibshirani, J. Friedman - Springer
This book brings together many of the important new ideas in learning, and explains them in a statistical framework. The authors emphasize the methods and their conceptual underpinnings rather than their theoretical properties.
(36174 views)
Elements of Causal Inference: Foundations and Learning Algorithmsby J. Peters, D. Janzing, B. Schölkopf - The MIT Press
This book offers a self-contained and concise introduction to causal models and how to learn them from data. The book teaches readers how to use causal models: how to compute intervention distributions, how to infer causal models from data ...
(3417 views)
Machine Learningby Abdelhamid Mellouk, Abdennacer Chebira - InTech
Neural machine learning approaches, Hamiltonian neural networks, similarity discriminant analysis, machine learning methods for spoken dialogue simulation and optimization, linear subspace learning for facial expression analysis, and more.
(13529 views)