Gaussian processes for machine learning

✍ Scribed by Williams, Christopher K. I.;Rasmussen, Carl Edward

Publisher: MIT Press
Year: 2008
Tongue: English
Leaves: 266
Series: Adaptive computation and machine learning
Edition: 3. print
Category: Library

No coin nor oath required. For personal study only.

✦ Subjects

Gauß-Prozess;Gaussian processes--Data processing;Machine learning--Mathematical models;Maschinelles Lernen;Gaussian processes -- Data processing;Machine learning -- Mathematical models

📜 SIMILAR VOLUMES

Gaussian Processes for Machine Learning

📁 Gaussian Processes for Machine Learning

✍ Carl Edward Rasmussen, Christopher K. I. Williams 📂 Library 📅 2005 🏛 The MIT Press 🌐 English

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 and prac

Gaussian processes for machine learning

📁 Gaussian processes for machine learning

✍ Rasmussen C.E., Williams C.K.I. 📂 Library 📅 2006 🏛 MIT 🌐 English

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 and p

Gaussian Processes for Machine Learning

📁 Gaussian Processes for Machine Learning

✍ Carl Edward Rasmussen; Christopher K.I. Williams 📂 Library 📅 2005 🏛 Mit Press 🌐 English

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

Gaussian Processes for Machine Learning

📁 Gaussian Processes for Machine Learning

✍ Carl Edward Rasmussen, Christopher K. I. Williams 📂 Library 📅 2006 🏛 MIT Press 🌐 English

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

Gaussian Processes for Machine Learning

📁 Gaussian Processes for Machine Learning

✍ Carl Edward Rasmussen, Christopher K. I. Williams 📂 Library 📅 2005 🏛 The MIT Press 🌐 English

Gaussian Processes for Machine Learning

📁 Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)

✍ Carl Edward Rasmussen, Christopher K. I. Williams 📂 Library 📅 2005 🏛 The MIT Press 🌐 English

A specific advantage of this book is that it is one of the few that dedicate a whole chapter on the connection between Bayesian methods using Gaussian Processes and Reproducing Kernel Hilbert Spaces. Even if this connection is a posteriori pretty obvious, it is nice to have it broken down clearly in