Summary
International Symposium on Nonlinear Theory and its Applications
2005
Session Number:1-2-1
Session:
Number:1-2-1-1
Can DT-CNN Classifiers outperform SVM?
Christian Merkwirth, Jochen Brocker, Jorg Wichard, Maciej Ogorza?ek,
pp.557-560
Publication Date:2005/10/18
Online ISSN:2188-5079
DOI:10.34385/proc.40.1-2-1-1
PDF download (83.4KB)
Summary:
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 employing concepts and techniques from Statistical Learning, we can obtain results outperforming that of a polynomial Support Vector Machine (SVM).