DocumentCode :
1817131
Title :
Pattern classifying neural network based on Fisher´s linear discriminant function
Author :
Lee, Jong Chan ; Kim, Yung Hwan ; Lee, Won Don ; Lee, Suk Hoon
Author_Institution :
Coll. of Natural Sci., ChungNam Nat. Univ., Daejun, South Korea
Volume :
1
fYear :
1992
fDate :
7-11 Jun 1992
Firstpage :
743
Abstract :
The network model for two-class pattern classification originally proposed by C. Koutsougeras and C.A. Papachristou (1988) is extended to n-class pattern classification. The proposed model has the advantage of expanding the network by adding the units during the partitioning of the input space while other models have the network topology specified. The result is compared with that of ID3, which is known as a knowledge acquisition tool in machine learning. The comparison shows that the proposed model leads to a better correct rate. This improvement might result from the additional consideration of the optimal projection direction, which ID3 does not consider
Keywords :
knowledge acquisition; learning (artificial intelligence); neural nets; pattern recognition; Fisher´s linear discriminant function; ID3; knowledge acquisition tool; machine learning; n-class pattern classification; network topology; partitioning; pattern classifying neural nets; Computer science; Hypercubes; Intelligent networks; Knowledge acquisition; Linear discriminant analysis; Machine learning; Neural networks; Neurons; Pattern classification; Statistics;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks, 1992. IJCNN., International Joint Conference on
Conference_Location :
Baltimore, MD
Print_ISBN :
0-7803-0559-0
Type :
conf
DOI :
10.1109/IJCNN.1992.287098
Filename :
287098
Link To Document :
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