Effects of Sample Size in Classifier Design
IEEE Transactions on Pattern Analysis and Machine Intelligence
C4.5: programs for machine learning
C4.5: programs for machine learning
Radial basis function networks 1: recent developments in theory and applications
Radial basis function networks 1: recent developments in theory and applications
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Three learning phases for radial-basis-function networks
Neural Networks
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
Data Mining Methods and Models
Data Mining Methods and Models
Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
Data Mining
Introduction to Neural Networks for C#, 2nd Edition
Introduction to Neural Networks for C#, 2nd Edition
SMOTE: synthetic minority over-sampling technique
Journal of Artificial Intelligence Research
A quantitative comparison of different MLP activation functions in classification
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
Decision trees can initialize radial-basis function networks
IEEE Transactions on Neural Networks
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It is known that generated knowledge models for data mining tasks are dependent upon supplied data sets, so supplying good data sets for target data mining algorithms is important for the success of data mining. Therefore, in order to find better RBF networks of k-means clustering efficiently, we refer to the number of errors that are from decision trees, and use the information to improve training data sets for RBF networks and we also refer to terminal nodes to initialize the k value. Experiments with real world data sets showed good results.