Communications of the ACM
Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Using neural networks for data mining
Future Generation Computer Systems - Special double issue on data mining
Machine Learning - Special issue on applications of machine learning and the knowledge discovery process
Data mining: concepts and techniques
Data mining: concepts and techniques
Machine Learning
Effective Data Mining Using Neural Networks
IEEE Transactions on Knowledge and Data Engineering
The Effects of Training Set Size on Decision Tree Complexity
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Unsupervised query-based learning of neural networks using selective-attention and self-regulation
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Detecting network intrusions using signal processing with query-based sampling filter
EURASIP Journal on Advances in Signal Processing - Special issue on signal processing applications in network intrusion detection systems
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Neural networks have advantages of the high tolerance to noisy data as well as the ability to classify patterns having not been trained. While being applied in data mining, the time required to induce models from large data sets are one of the most important considerations. In this paper, we introduce a query-based learning scheme to improve neural networks' performance in data mining. Results show that the proposed algorithm can significantly reduce the training set cardinality. Additionally, the quality of training results can be also ensured. Our future work is to apply this concept to other data mining schemes and applications.