Perceptrons: expanded edition
Effects of Sample Size in Classifier Design
IEEE Transactions on Pattern Analysis and Machine Intelligence
Small Sample Size Effects in Statistical Pattern Recognition: Recommendations for Practitioners
IEEE Transactions on Pattern Analysis and Machine Intelligence
Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
Efficient progressive sampling
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Discretization: An Enabling Technique
Data Mining and Knowledge Discovery
Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
Function approximation using artificial neural networks
WSEAS Transactions on Mathematics
Use of RBF neural network in EMG signal noise removal
WSEAS Transactions on Circuits and Systems
Control of a differentially driven mobile robot using radial basis function based neural networks
WSEAS Transactions on Systems and Control
Data Mining Methods and Models
Data Mining Methods and Models
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
Hi-index | 0.00 |
Even though radial basis function networks are known to have good prediction accuracy in several domains, it is not known to decide a proper sample size like other data mining algorithms, so the task of deciding proper sample sizes for the networks tends to be arbitrary. As the size of samples grows, the improvement in error rates becomes better slowly. But we cannot use larger and larger samples, because we have limited training examples, and there is some fluctuation in accuracy depending on the sample sizes. This paper suggests a progressive resampling technique to cope with the fluction of prediction accuracy values for better radial basis function networks. The suggestion is proved by experiments with promising results.