Combining classifiers based on confidence values

Author

Atukorale, Ajantha S. ; Suganthan, P.N.

Author_Institution

Dept. of Comput. Sci. & Electr. Eng., Queensland Univ., Brisbane, Qld., Australia

fYear

1999

fDate

20-22 Sep 1999

Firstpage

37

Lastpage

40

Abstract

The paper describes our investigation into the neural gas (NG) network algorithm and the hierarchical overlapped architecture (HONG) which we have built by retaining the essence of the original NG algorithm. By defining an implicit ranking scheme, the NG algorithm was made to run faster in its sequential implementation. Each HONG network generated multiple classifications for every sample data presented as confidence values. These confidence values were combined to obtain the final classification of the HONG architecture. Three HONG networks based on three different feature sets with global and structural features were also trained to obtain better classification on conflicting handwritten data. An excellent recognition rate for the NIST SD3 database was consequently obtained

Keywords

handwriting recognition; handwritten character recognition; learning (artificial intelligence); neural net architecture; neural nets; pattern classification; HONG networks; NG algorithm; NIST SD3 database; classifiers; confidence values; feature sets; handwritten data; hierarchical overlapped architecture; implicit ranking scheme; multiple classifications; neural gas network algorithm; recognition rate; sample data; sequential implementation; structural features; Character recognition; Computer science; Feature extraction; Handwriting recognition; Lattices; Marine vehicles; NIST; Radio access networks;

fLanguage

English

Publisher

ieee

Conference_Titel

Document Analysis and Recognition, 1999. ICDAR '99. Proceedings of the Fifth International Conference on

Conference_Location

Bangalore

Print_ISBN

0-7695-0318-7

Type

conf

DOI

10.1109/ICDAR.1999.791719

Filename

791719