Conditional information analysis

Author

Kamimura, Ryotaro

Author_Institution

Inf. Sci. Lab., Tokai Univ., Kanagawa

Volume

1

fYear

2000

fDate

2000

Firstpage

197

Abstract

We propose a conditional information analysis in which information on important patterns is selectively detected. The selection is realized by α-information which can be used to maximize or minimize selectively conditional information, according to the important or characteristics of input patterns. The information analysis was applied to two feature detection problems: an alphabet character recognition and medical data. In both problems, experimental results confirmed that conditional information is flexibly maximized or minimized, depending upon input patterns. They also showed that the conditional information is a good measure to distinguish between different classes

Keywords

character recognition; feature extraction; information analysis; medical computing; neural nets; optimisation; alphabet character recognition; conditional information analysis; feature extraction; medical data; neural networks; optimisation; Computer vision; Control systems; Data mining; Feature extraction; Filtering; Information analysis; Information science; Laboratories; Pattern analysis; Uncertainty;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 2000. IJCNN 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on

Conference_Location

Como

ISSN

1098-7576

Print_ISBN

0-7695-0619-4

Type

conf

DOI

10.1109/IJCNN.2000.857836

Filename

857836