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Learning internal representations by error propagation
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IEEE Transactions on Pattern Analysis and Machine Intelligence
Universal approximation using radial-basis-function networks
Neural Computation
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KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Radial basis function networks 1: recent developments in theory and applications
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Guide to Neural Computing Applications
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Neural Networks for Pattern Recognition
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IEEE Transactions on Pattern Analysis and Machine Intelligence
Data Mining Methods and Models
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Introduction to Data Mining, (First Edition)
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Neural Computation
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Knowledge and Information Systems
A comparative analysis of artificial neural network technologies in intrusion detection systems
MIV'06 Proceedings of the 6th WSEAS International Conference on Multimedia, Internet & Video Technologies
Implementation feasibility of convex recursive deletion regions using multi-layer perceptrons
WSEAS Transactions on Computers
Function approximation using artificial neural networks
WSEAS Transactions on Mathematics
WSEAS Transactions on Information Science and Applications
Introduction to Neural Networks for C#, 2nd Edition
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Machine Learning: An Algorithmic Perspective
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IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Evolutionary optimization of radial basis function classifiers for data mining applications
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Probabilistic neural-network structure determination for pattern classification
IEEE Transactions on Neural Networks
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Even though multilayer perceptrons and radial basis function networks belong to the class of artificial neural networks and they are used for similar tasks, they have very different structures and training mechanisms. So, some researchers showed better performance with radial basis function networks, while others showed some different results with multilayer perceptrons. This paper compares the classification accuracy of the two neural networks with respect to training data set size, and shows the performance of the two neural networks can be differently dependent on training data set size. Experiments show the tendency that multilayer perceptrons have better performance in relatively larger training data sets for some data sets, even though radial basis function networks have better performance in relatively smaller training set size for the same data sets. The experiment was done with four real world data sets.