Districted matching approach for 1D object classification

Author

Chen, Liang ; Nilufar, Sharmin ; Kwan, H.K.

Author_Institution

Comput. Sci. Dept., Univ. of Northern British Columbia, Prince George, BC, Canada

fYear

2004

fDate

20-22 Oct. 2004

Firstpage

206

Lastpage

209

Abstract

This paper proves that the districted matching scheme is more stable than undistricted matching scheme for pattern classification applications, where an object to be classified consists of elements lying on a limited line segment in 1D space. The theoretical result suggests the using of districted matching schemes for pattern recognition/recognition of 1D objects. The method is used in the predication of start codons of nucleotide sequences by artificial neural network based approaches.

Keywords

biology computing; learning (artificial intelligence); neural nets; object recognition; pattern classification; sequences; 1D object classification; 1D object recognition; artificial neural network; districted matching; limited line segment; nucleotide sequences; pattern classification; pattern recognition; start codons; supervised machine learning; Application software; Artificial neural networks; Computer science; Feature extraction; Neural networks; Pattern classification; Pattern matching; Pattern recognition; Pollution; Voting;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Multimedia, Video and Speech Processing, 2004. Proceedings of 2004 International Symposium on

Print_ISBN

0-7803-8687-6

Type

conf

DOI

10.1109/ISIMP.2004.1434036

Filename

1434036