TY - GEN
T1 - Offline recognition of handwritten numeral characters with polynomial neural networks using topological features
AU - El-Alfy, El Sayed M.
PY - 2010
Y1 - 2010
N2 - 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.
AB - 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.
KW - GMDH
KW - Handwritten numeral character recognition
KW - Machine learning
KW - Non-Gaussian topological features
KW - Pattern recognition
KW - Polynomial networks
UR - https://www.scopus.com/pages/publications/77953733303
U2 - 10.1007/978-3-642-13059-5_18
DO - 10.1007/978-3-642-13059-5_18
M3 - Conference contribution
AN - SCOPUS:77953733303
SN - 3642130585
SN - 9783642130588
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 173
EP - 183
BT - Advances in Artificial Intelligence - 23rd Canadian Conference on Artificial Intelligence, Canadian AI 2010, Proceedings
ER -