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.
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El-Alfy, ES.M. (2010). Offline Recognition of Handwritten Numeral Characters with Polynomial Neural Networks Using Topological Features. In: Farzindar, A., Kešelj, V. (eds) Advances in Artificial Intelligence. Canadian AI 2010. Lecture Notes in Computer Science(), vol 6085. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13059-5_18
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DOI: https://doi.org/10.1007/978-3-642-13059-5_18
Publisher Name: Springer, Berlin, Heidelberg
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