DocumentCode :
2628814
Title :
The handling of don´t care attributes
Author :
Lee, Hahn-Ming ; Hsu, Ching-Chi
Author_Institution :
Dept. of Comput. Sci. & Inf. Eng., Nat. Taiwan Univ., Taipei, Taiwan
fYear :
1991
fDate :
18-21 Nov 1991
Firstpage :
1085
Abstract :
A critical factor that affects the performance of neural network training algorithms and the generalization of trained networks is the training instances. The authors consider the handling of don´t care attributes in training instances. Several approaches are discussed and their experimental results are presented. The following approaches are considered: (1) replace don´t care attributes with a fixed value; (2) replace don´t care attributes with their maximum or minimum encoded values; (3) replace don´t care attributes with their maximum and minimum encoded values; and (4) replace don´t care attributes with all their possible encoded values
Keywords :
neural nets; don´t care attributes; encoded values; fixed value; neural network training; Computational intelligence; Computer science; Electronic mail; Expert systems; Information processing; Machine intelligence; Neural networks;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks, 1991. 1991 IEEE International Joint Conference on
Print_ISBN :
0-7803-0227-3
Type :
conf
DOI :
10.1109/IJCNN.1991.170539
Filename :
170539
Link To Document :
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