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A Formulation for Active Learning with Applications to Object Detection

We discuss a formulation for active example selection for function learning problems. This formulation is obtained by adapting Fedorov's optimal experiment design to the learning problem. We specifically show how to analytically derive example selection algorithms for certain well defined function classes. We then explore the behavior and sample complexity of such active learning algorithms. Finally, we view object detection as a special case of function learning and show how our formulation reduces to a useful heuristic to choose examples to reduce the generalization error.

Identiferoai:union.ndltd.org:MIT/oai:dspace.mit.edu:1721.1/7209
Date06 June 1996
CreatorsSung, Kah Kay, Niyogi, Partha
Source SetsM.I.T. Theses and Dissertation
Languageen_US
Detected LanguageEnglish
Format40 p., 593069 bytes, 1090749 bytes, application/octet-stream, application/pdf
RelationAIM-1438, CBCL-116

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