Combining multiple neural networks by fuzzy integral for robust classification

1995 ◽  
Vol 25 (2) ◽  
pp. 380-384 ◽  
Author(s):  
Sung-Bae Cho ◽  
J.H. Kim
Keyword(s):  
Neural Networks ◽  
Fuzzy Integral ◽  
Robust Classification ◽  
Multiple Neural Networks

Application of Choquet Integral to Efficient Aggregation of Neural Networks

2000 ◽  
Vol 4 (1) ◽  
pp. 52-56
Author(s):  
Sung-Bae Cho ◽  
Keyword(s):  
Neural Networks ◽  
Pattern Recognition ◽  
Artificial Neural Networks ◽  
Choquet Integral ◽  
Subjective Evaluation ◽  
Experimental Results ◽  
Recognition Problem ◽  
Fuzzy Integral ◽  
Multiple Neural Networks ◽  
The Individual

This paper presents a novel application of fuzzy integral to combine multiple neural networks subjectively. The concept of combining multiple neural networks or classifiers has been largely exploited in the fields of artificial neural networks and pattern recognition. The proposed method based on Choquet integral nonlinearly combines the outputs of the networks with subjective evaluation of the reliability of the individual neural networks. Experimental results with the recognition problem of totally unconstrained handwritten numerals show that the performance of the proposed method outperforms that of the conventional methods.

Robust Classification of "Artificial Nose" Sensor Data by Artificial Neural Networks

1997 ◽  
Vol 30 (9) ◽  
pp. 347-351 ◽  
Author(s):  
Z. Boger ◽  
L. Ratton ◽  
T.A. Kunt ◽  
T.J. Mc Avoy ◽  
R.E. Cavicchi ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Sensor Data ◽  
Robust Classification ◽  
Artificial Nose ◽  
Artificial Neural
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