Uncertainty in Artificial Intelligence
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Normalized Online Learning
Stephane Ross, Paul Mineiro, John Langford
Abstract:
We introduce online learning algorithms which are independent of feature scales, proving regret bounds dependent on the ratio of scales existent in the data rather than the absolute scale. This has several useful effects: there is no need to pre-normalize data, the test-time and test-space complexity are reduced, and the algorithms are more robust.
Keywords:
Pages: 537-545
PS Link:
PDF Link: /papers/13/p537-ross.pdf
BibTex:
@INPROCEEDINGS{Ross13,
AUTHOR = "Stephane Ross and Paul Mineiro and John Langford",
TITLE = "Normalized Online Learning",
BOOKTITLE = "Proceedings of the Twenty-Ninth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-13)",
PUBLISHER = "AUAI Press",
ADDRESS = "Corvallis, Oregon",
YEAR = "2013",
PAGES = "537--545"
}


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