TY - GEN
T1 - Multiple network architecture combined by fuzzy integral
AU - Cho, Sung Bae
AU - Kim, Jin H.
PY - 1993
Y1 - 1993
N2 - Recently, in the area of artificial neural network, the concept of combining multiple networks has been proposed as a new direction for the development of highly reliable neural network systems. In this paper we propose a method for multinetwork combination based on the fuzzy integral. This technique nonlinearly combines objective evidence. in the form of a fuzzy membership function, with subjective evaluation of the worth of the individual neural networks with respect to the decision. The experimental results with the recognition problem of on-line handwriting characters show that the performance of individual networks could be improved significantly.
AB - Recently, in the area of artificial neural network, the concept of combining multiple networks has been proposed as a new direction for the development of highly reliable neural network systems. In this paper we propose a method for multinetwork combination based on the fuzzy integral. This technique nonlinearly combines objective evidence. in the form of a fuzzy membership function, with subjective evaluation of the worth of the individual neural networks with respect to the decision. The experimental results with the recognition problem of on-line handwriting characters show that the performance of individual networks could be improved significantly.
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M3 - Conference contribution
AN - SCOPUS:0027815097
SN - 0780314212
T3 - Proceedings of the International Joint Conference on Neural Networks
SP - 1373
EP - 1376
BT - Proceedings of the International Joint Conference on Neural Networks
A2 - Anon, null
PB - Publ by IEEE
T2 - Proceedings of 1993 International Joint Conference on Neural Networks. Part 2 (of 3)
Y2 - 25 October 1993 through 29 October 1993
ER -