Interactive email filtering: Learning from misclassified examples


Autoria(s): Chen, D.; Li, X.; Dong, Z. Y.; Smith, P.A.
Contribuinte(s)

S. Ge

K. Tan

Data(s)

01/01/2004

Resumo

Learning from mistakes has proven to be an effective way of learning in the interactive document classifications. In this paper we propose an approach to effectively learning from mistakes in the email filtering process. Our system has employed both SVM and Winnow machine learning algorithms to learn from misclassified email documents and refine the email filtering process accordingly. Our experiments have shown that the training of an email filter becomes much effective and faster

Identificador

http://espace.library.uq.edu.au/view/UQ:100341

Idioma(s)

eng

Publicador

IEEE

Palavras-Chave #E1 #280103 Information Storage, Retrieval and Management #700103 Information processing services
Tipo

Conference Paper