Offline Recognition of Handwritten Numeral Characters with Polynomial Neural Networks Using Topological Features
Identifieur interne : 000620 ( Main/Exploration ); précédent : 000619; suivant : 000621Offline Recognition of Handwritten Numeral Characters with Polynomial Neural Networks Using Topological Features
Auteurs : M. El-Alfy [Arabie saoudite]Source :
- Lecture Notes in Computer Science [ 0302-9743 ] ; 2010.
Abstract
Abstract: Group-Method of Data Handling (GMDH) has been recognized as a powerful tool in machine learning. It has the potential to build predictive neural network models of polynomial functions using only a reduced set of features which minimizes the prediction error. This paper explores the offline recognition of isolated handwritten numeral characters described with non-Gaussian topological features using GMDH-based polynomial networks. In order to study the effectiveness of the proposed approach, we apply it on a publicly available dataset of isolated handwritten numerals and compare the results with five other state-of-the-art classifiers: multilayer Perceptron, support-vector machine, radial-basis function, naïve Bayes and rule-based classifiers. In addition to improving the classification accuracy and the per-class performance measures, using GMDH-based polynomial neural networks has led to significant feature dimensionality reduction.
Url:
DOI: 10.1007/978-3-642-13059-5_18
Affiliations:
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<front><div type="abstract" xml:lang="en">Abstract: Group-Method of Data Handling (GMDH) has been recognized as a powerful tool in machine learning. It has the potential to build predictive neural network models of polynomial functions using only a reduced set of features which minimizes the prediction error. This paper explores the offline recognition of isolated handwritten numeral characters described with non-Gaussian topological features using GMDH-based polynomial networks. In order to study the effectiveness of the proposed approach, we apply it on a publicly available dataset of isolated handwritten numerals and compare the results with five other state-of-the-art classifiers: multilayer Perceptron, support-vector machine, radial-basis function, naïve Bayes and rule-based classifiers. In addition to improving the classification accuracy and the per-class performance measures, using GMDH-based polynomial neural networks has led to significant feature dimensionality reduction.</div>
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