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Employing Neocognitron Neural Network Base Ensemble Classifiers To Enhance Efficiency of Classification In Handwritten Digit Datasets

ISSN : 2231 - 5403
Journal from gdlhub / 2017-08-14 11:52:33
Oleh : Neera Saxena, Qasima Abbas Kazmi, Chandra Pal and O.P. Vyas, Computer Science & Information Technology
Dibuat : 2012-06-26, dengan 1 file

Keyword : Classifier ensemble, Error Back-Propagation, Multiple Classifier Combination, Neocognitron, Neural Networks, Pattern Recognition.
Subjek : Employing Neocognitron Neural Network Base Ensemble Classifiers To Enhance Efficiency of Classification In Handwritten Digit Datasets
Url : http://airccj.org/CSCP/vol1/csit1236.pdf
Sumber pengambilan dokumen : Internet

This paper presents an ensemble of neo-cognitron neural network base classifiers to enhance


the accuracy of the system, along the experimental results. The method offers lesser


computational preprocessing in comparison to other ensemble techniques as it ex-preempts


feature extraction process before feeding the data into base classifiers. This is achieved by the


basic nature of neo-cognitron, it is a multilayer feed-forward neural network. Ensemble of such


base classifiers gives class labels for each pattern that in turn is combined to give the final class


label for that pattern. The purpose of this paper is not only to exemplify learning behaviour of


neo-cognitron as base classifiers, but also to purport better fashion to combine neural network


based ensemble classifiers.

Deskripsi Alternatif :

This paper presents an ensemble of neo-cognitron neural network base classifiers to enhance


the accuracy of the system, along the experimental results. The method offers lesser


computational preprocessing in comparison to other ensemble techniques as it ex-preempts


feature extraction process before feeding the data into base classifiers. This is achieved by the


basic nature of neo-cognitron, it is a multilayer feed-forward neural network. Ensemble of such


base classifiers gives class labels for each pattern that in turn is combined to give the final class


label for that pattern. The purpose of this paper is not only to exemplify learning behaviour of


neo-cognitron as base classifiers, but also to purport better fashion to combine neural network


based ensemble classifiers.

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