In this study, we compare the performance of well-known neural networks, namely, back-propagation (BP) algorithm, Neuro-Fuzzy network and Support Vector Machine (SVM) using the standard three database sets: Wisconsin breast cancer, Iris and wine data. Since such database have been useful for evaluating performance of a group of machine learning algorithms, a series of experiments have been carried out for three algorithms using the cross validation method. Results suggest that SVM outperforms the others and the Neuro-Fuzzy network is better than the BP algorithm for this data set. © Springer-Verlag Berlin Heidelberg 2006.
CITATION STYLE
Kim, Y. G., Jang, M. S., Cho, K. S., & Park, G. T. (2006). Performance comparison between backpropagation, neuro-fuzzy network, and SVM. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3967 LNCS, pp. 438–446). Springer Verlag. https://doi.org/10.1007/11753728_44
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