Stochastic Gradient Descent Training of Ensembles of DT-CNN Classifiers for 
Digit Recognition

Merkwirth, Christian; Ogorzalek, Maciej; Wichard, Jörg Daniel; Ogorzalek, Maciej; Galias, Zbigniew; Garda, Bartłomiej; Kadeja, Bartłomiej

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Stochastic Gradient Descent Training of Ensembles of DT-CNN Classifiers for Digit Recognition

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Merkwirth, Christian
Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society;

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Citation

Merkwirth, C., Ogorzalek, M., & Wichard, J. D. (2003). Stochastic Gradient Descent Training of Ensembles of DT-CNN Classifiers for Digit Recognition. In Proceedings of the 16th European Conference on Circuit Theory and Design ECCTD'03 (pp. 337-341). Krakow, Poland: Faculty of Electrical Engineering, Automatics, Computer Science and Electronics, AGH University of Science and Technology.

Cite as: https://hdl.handle.net/11858/00-001M-0000-000F-2E34-C

Abstract

We show how to train Discrete Time Cellular Neural Networks (DT-CNN) successfully by backpropagation to perform pattern recognition on a data set of handwritten digits. By using concepts and techniques from Machine Learning, we can outperform Support Vector Machines (SVM) on this problem.