@inproceedings{7021d20889c341e5a94814183fe595a6,
title = "Online Updating for Gaussian Process Learning",
abstract = "Gaussian processes for regression and classification have become an effective machine learning methodology with a number of distinctive advantages. One notable disadvantage of Gaussian process methods is the computational complexity related to the inversion of matrices, especially for applications that involve large datasets. In this paper, an exact online updating algorithm is presented to significantly reduce the amount of computations for repeated progressive trainings of Gaussian processes.",
keywords = "Cholesky decomposition, Gaussian process, Woodbury formula, matrix inverse, online algorithm",
author = "Hongjun Su and Hong Zhang",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 2017 International Conference on Computational Science and Computational Intelligence, CSCI 2017 ; Conference date: 14-12-2017 Through 16-12-2017",
year = "2018",
month = dec,
day = "4",
doi = "10.1109/CSCI.2017.28",
language = "English",
series = "Proceedings - 2017 International Conference on Computational Science and Computational Intelligence, CSCI 2017",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "180--183",
editor = "Tinetti, {Fernando G.} and Quoc-Nam Tran and Leonidas Deligiannidis and Yang, {Mary Qu} and Yang, {Mary Qu} and Arabnia, {Hamid R.}",
booktitle = "Proceedings - 2017 International Conference on Computational Science and Computational Intelligence, CSCI 2017",
address = "United States",
}