Effects of Sample Size in Classifier Design
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Small Sample Size Effects in Statistical Pattern Recognition: Recommendations for Practitioners
IEEE Transactions on Pattern Analysis and Machine Intelligence
C4.5: programs for machine learning
C4.5: programs for machine learning
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
PUBLIC: A Decision Tree Classifier that Integrates Building and Pruning
Data Mining and Knowledge Discovery
Discretization: An Enabling Technique
Data Mining and Knowledge Discovery
SLIQ: A Fast Scalable Classifier for Data Mining
EDBT '96 Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology
SPRINT: A Scalable Parallel Classifier for Data Mining
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
| Hi-index | 0.00 |
Neural networks have been developed for machine learning and data mining tasks, and because data mining problems contain a large amount of data, sampling is a necessity for the success of the task. For this reason, this paper suggests an effective sampling technique that is based on a generated decision tree, where the trees are generated based on a fast and dirty tree generation algorithm. Experiments with several sample sizes and RBF network showed that the method is more effective with respect to accuracy than conventional random sampling method.