Gaussian Processes in Machine Learning. Rasmussen, C. E. In Bousquet, O., von Luxburg, U., & Rätsch, G., editors, Advanced Lectures on Machine Learning: ML Summer Schools 2003, Canberra, Australia, February 2 - 14, 2003, Tübingen, Germany, August 4 - 16, 2003, Revised Lectures, pages 63–71. Springer Berlin Heidelberg, Berlin, Heidelberg, 2004. ![Gaussian Processes in Machine Learning [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex We give a basic introduction to Gaussian Process regression models. We focus on understanding the role of the stochastic process and how it is used to define a distribution over functions. We present the simple equations for incorporating training data and examine how to learn the hyperparameters using the marginal likelihood. We explain the practical advantages of Gaussian Process and end with conclusions and a look at the current trends in GP work.
@incollection{rasmussen2004,
address = {Berlin, Heidelberg},
title = {Gaussian {Processes} in {Machine} {Learning}},
isbn = {978-3-540-28650-9},
url = {https://doi.org/10.1007/978-3-540-28650-9_4},
abstract = {We give a basic introduction to Gaussian Process regression models. We focus on understanding the role of the stochastic process and how it is used to define a distribution over functions. We present the simple equations for incorporating training data and examine how to learn the hyperparameters using the marginal likelihood. We explain the practical advantages of Gaussian Process and end with conclusions and a look at the current trends in GP work.},
booktitle = {Advanced {Lectures} on {Machine} {Learning}: {ML} {Summer} {Schools} 2003, {Canberra}, {Australia}, {February} 2 - 14, 2003, {Tübingen}, {Germany}, {August} 4 - 16, 2003, {Revised} {Lectures}},
publisher = {Springer Berlin Heidelberg},
author = {Rasmussen, Carl Edward},
editor = {Bousquet, Olivier and von Luxburg, Ulrike and Rätsch, Gunnar},
year = {2004},
doi = {10.1007/978-3-540-28650-9_4},
pages = {63--71},
}
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