Online Updating for Gaussian Process Learning
Document Type
Conference Proceeding
Publication Date
12-6-2018
Publication Title
2017 International Conference on Computational Science and Computational Intelligence (CSCI)
DOI
10.1109/CSCI.2017.28
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.
Recommended Citation
Su, Hongjun, Hong Zhang.
2018.
"Online Updating for Gaussian Process Learning."
2017 International Conference on Computational Science and Computational Intelligence (CSCI).
doi: 10.1109/CSCI.2017.28
https://digitalcommons.georgiasouthern.edu/compsci-facpubs/319
