A Formulation for Active Learning with Applications to Object Detection


Autoria(s): Sung, Kah Kay; Niyogi, Partha
Data(s)

20/10/2004

20/10/2004

06/06/1996

Resumo

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.

Formato

40 p.

593069 bytes

1090749 bytes

application/octet-stream

application/pdf

Identificador

AIM-1438

CBCL-116

http://hdl.handle.net/1721.1/7209

Idioma(s)

en_US

Relação

AIM-1438

CBCL-116

Palavras-Chave #active learning #optimal experiment design #object detection #example selection