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Normalized Online Learning

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arxiv 1408.2065 v1 pith:I6626GJJ submitted 2014-08-09 cs.LG stat.ML

Normalized Online Learning

Stephane Ross , Paul Mineiro , John Langford This is my paper
classification cs.LG stat.ML
keywords algorithmsdatalearningonlinescalesabsoluteboundscomplexity
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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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.

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