A Formulation for Active Learning with Applications to Object Detection

dc.creator	Sung, Kah Kay
dc.creator	Niyogi, Partha
dc.date	2004-10-20T20:49:52Z
dc.date	2004-10-20T20:49:52Z
dc.date	1996-06-06
dc.date.accessioned	2013-10-09T02:48:34Z
dc.date.available	2013-10-09T02:48:34Z
dc.date.issued	2013-10-09
dc.identifier	AIM-1438
dc.identifier	CBCL-116
dc.identifier	http://hdl.handle.net/1721.1/7209
dc.identifier.uri	http://koha.mediu.edu.my:8181/xmlui/handle/1721
dc.description	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.
dc.format	40 p.
dc.format	593069 bytes
dc.format	1090749 bytes
dc.format	application/octet-stream
dc.format	application/pdf
dc.language	en_US
dc.relation	AIM-1438
dc.relation	CBCL-116
dc.subject	active learning
dc.subject	optimal experiment design
dc.subject	object detection
dc.subject	example selection
dc.title	A Formulation for Active Learning with Applications to Object Detection

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