Rasmussen, CE
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
- ISBN 13:
- 9780262182539
- author:
- Rasmussen, CE
- format:
- HardBack
- publisher:
- MIT Press
- language:
- English
- Publication Year:
- 2006
- Pages:
- 266
- Dimensions:
- 20.32 x 2.54 x 25.4 centimetres
- Genre:
- Psychology
- Condition:
- New
- Availability:
- Item usually sent within 10 working days
Description
Gaussian Processes for Machine Learning provides a comprehensive and self-contained introduction to Gaussian processes, a principled approach to learning in kernel machines. This book offers a systematic treatment of theoretical and practical aspects of Gaussian processes in machine learning, covering supervised-learning problems for regression and classification, as well as model selection from both Bayesian and classical perspectives. Gaussian processes have gained increased attention in the machine-learning community over the past decade, and this book fills a need for a unified treatment. The author discusses various covariance (kernel) functions, their properties, and connections to other techniques such as support-vector machines and neural networks. Theoretical issues are also addressed, including learning curves and the PAC-Bayesian framework. With detailed algorithms, illustrative examples, and exercises, this book is suitable for researchers and students in machine learning and applied statistics. Code and datasets are available online, providing a valuable resource for those looking to explore Gaussian processes further.
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