Training a Φ-machine classifier using feature scaling-space

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Efficient classification of signal patterns plays a vital role in data mining and other computational intelligence applications. This paper presents a reciprocal-sigmoid model for pattern classification. The proposed classifier can be considered as a «-machine since it preserves the theoretical advantage of linear machines where the weight parameters can be estimated in a single step. To handle possible over-fitting when using high order models, the classifier is trained using multiple samples of uniformly scaled pattern features. The classifier is empirically evaluated using benchmark data sets for statistical evidence.

Original languageEnglish
Title of host publication2006 IEEE International Conference on Industrial Informatics, INDIN'06
PublisherIEEE Computer Society
Pages1334-1339
Number of pages6
ISBN (Print)0780397010, 9780780397019
DOIs
Publication statusPublished - 2006 Jan 1
Event2006 IEEE International Conference on Industrial Informatics, INDIN'06 - Singapore, Singapore
Duration: 2006 Aug 162006 Aug 18

Publication series

Name2006 IEEE International Conference on Industrial Informatics, INDIN'06

Other

Other2006 IEEE International Conference on Industrial Informatics, INDIN'06
CountrySingapore
CitySingapore
Period06/8/1606/8/18

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering

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