Fast Gaussian Process Regression using KD-Trees
2006
Conference Paper
ei
The computation required for Gaussian process regression with n training examples is about O(n3) during training and O(n) for each prediction. This makes Gaussian process regression too slow for large datasets. In this paper, we present a fast approximation method, based on kd-trees, that significantly reduces both the prediction and the training times of Gaussian process regression.
Author(s): | Shen, Y. and Ng, AY. and Seeger, M. |
Book Title: | Advances in neural information processing systems 18 |
Journal: | Advances in Neural Information Processing Systems 18: Proceedings of the 2005 Conference |
Pages: | 1225-1232 |
Year: | 2006 |
Month: | May |
Day: | 0 |
Editors: | Weiss, Y. , B. Sch{\"o}lkopf, J. Platt |
Publisher: | MIT Press |
Department(s): | Empirical Inference |
Bibtex Type: | Conference Paper (inproceedings) |
Event Name: | Nineteenth Annual Conference on Neural Information Processing Systems (NIPS 2005) |
Event Place: | Vancouver, BC, Canada |
Address: | Cambridge, MA, USA |
Digital: | 0 |
ISBN: | 0-262-23253-7 |
Language: | en |
Organization: | Max-Planck-Gesellschaft |
School: | Biologische Kybernetik |
Links: |
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BibTex @inproceedings{3606, title = {Fast Gaussian Process Regression using KD-Trees}, author = {Shen, Y. and Ng, AY. and Seeger, M.}, journal = {Advances in Neural Information Processing Systems 18: Proceedings of the 2005 Conference}, booktitle = {Advances in neural information processing systems 18}, pages = {1225-1232}, editors = {Weiss, Y. , B. Sch{\"o}lkopf, J. Platt}, publisher = {MIT Press}, organization = {Max-Planck-Gesellschaft}, school = {Biologische Kybernetik}, address = {Cambridge, MA, USA}, month = may, year = {2006}, doi = {}, month_numeric = {5} } |