Nonmyopic ϵ-Bayes-Optimal Active Learning of Gaussian Processes
A fundamental issue in active learning of Gaussian processes is that of the exploration-exploitation trade-off. This paper presents a novel nonmyopic ϵ-Bayes-optimal active learning (ϵ-BAL) approach that jointly and naturally optimizes the trade-off. In contrast, existing works have primarily develo...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | en_US |
| Published: |
Association for Computing Machinery (ACM)
2015
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| Online Access: | http://hdl.handle.net/1721.1/100444 https://orcid.org/0000-0002-8585-6566 |