A Formulation for Active Learning with Applications to Object Detection

Please use this identifier to cite or link to this item: http://dspace.mediu.edu.my:8181/xmlui/handle/1721.1/7209

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DC Field	Value	Language
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	-
Appears in Collections:	MIT Items

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