Online learning for parameter selection in large scale image search
- Creators
- Aly, Mohamed
Abstract
We explore using online learning for selecting the best parameters of Bag of Words systems when searching large scale image collections. We study two algorithms for no regret online learning: Hedge algorithm that works in the full information setting, and Exp3 that works in the bandit setting. We use these algorithms for parameter selection in two scenarios: (a) using a training set to obtain weights for the different parameters, then either choosing the parameter setting with maximum weight or combining their results with weighted majority vote; (b) working fully online by selecting a parameter combination at every time step. We demonstrate the usefulness of online learning using experiments on four different real world datasets.
Additional Information
© 2010 IEEE. This research was supported by ONR grant N00173-09-C-4005.Attached Files
Published - 05543758.pdf
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Additional details
- Eprint ID
- 75124
- Resolver ID
- CaltechAUTHORS:20170314-165620992
- N00173-09-C-4005
- Office of Naval Research (ONR)
- Created
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2017-03-16Created from EPrint's datestamp field
- Updated
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2021-11-15Created from EPrint's last_modified field