The objective of data reduction is to obtain a compact representation of a large data set to facilitate repeated use of non-redundant information with complex and slow learning alg...
In order to discover relevant weights of neural networks, this paper proposes a novel method to learn a distinct squared penalty factor for each weight as a minimization problem ov...
Detection of interactions among data items constitutes an essential part of knowledge discovery. The cascade model is a rule induction methodology using levelwise expansion of a la...
Combining multiple global models (e.g. back-propagation based neural networks) is an effective technique for improving classification accuracy by reducing a variance through manipu...
This paper addresses personal E-mail filtering by casting it in the framework of text classification. Modeled as semi-structured documents, Email messages consist of a set of field...
In clustering, global feature selection algorithms attempt to select a common feature subset that is relevant to all clusters. Consequently, they are not able to identify individu...