Journal ArticleDOI
Combining multiple neural networks by fuzzy integral for robust classification
Sung-Bae Cho,Jong-Sung Kim +1 more
- Vol. 25, Iss: 2, pp 380-384
TLDR
The authors propose a method for multinetwork combination based on the fuzzy integral that 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.Abstract:
In the area of artificial neural networks, the concept of combining multiple networks has been proposed as a new direction for the development of highly reliable neural network systems. The authors 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 confirm the superiority of the presented method to the other voting techniques. >read more
Citations
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Journal ArticleDOI
On combining classifiers
TL;DR: A common theoretical framework for combining classifiers which use distinct pattern representations is developed and it is shown that many existing schemes can be considered as special cases of compound classification where all the pattern representations are used jointly to make a decision.
MonographDOI
Combining Pattern Classifiers
TL;DR: This combining pattern classifiers methods and algorithms helps people to enjoy a good book with a cup of coffee in the afternoon, instead they cope with some harmful virus inside their computer.
Journal ArticleDOI
Online and off-line handwriting recognition: a comprehensive survey
TL;DR: The nature of handwritten language, how it is transduced into electronic data, and the basic concepts behind written language recognition algorithms are described.
Journal ArticleDOI
Ensemble based systems in decision making
TL;DR: Conditions under which ensemble based systems may be more beneficial than their single classifier counterparts are reviewed, algorithms for generating individual components of the ensemble systems, and various procedures through which the individual classifiers can be combined are reviewed.
Decision templates for multiple classi"er fusion: an experimental comparison
TL;DR: This work presents here a simple rule for adapting the class combiner to the application and shows that decision templates based on integral type measures of similarity are superior to the other schemes on both data sets.
References
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