Title :
Conditional information analysis
Author :
Kamimura, Ryotaro
Author_Institution :
Inf. Sci. Lab., Tokai Univ., Kanagawa
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;
Conference_Titel :
Neural Networks, 2000. IJCNN 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on
Conference_Location :
Como
Print_ISBN :
0-7695-0619-4
DOI :
10.1109/IJCNN.2000.857836
