Extracting Refined Rules from Knowledge-Based Neural Networks
Machine Learning
Extracting rules from neural networks by pruning and hidden-unit splitting
Neural Computation
Fuzzy hypotheses for GUHA implications
Fuzzy Sets and Systems
Extraction of Logical Rules from Neural Networks
Neural Processing Letters
Data mining methods for knowledge discovery
Data mining methods for knowledge discovery
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Pattern Recognition and Neural Networks
Pattern Recognition and Neural Networks
Effective Data Mining Using Neural Networks
IEEE Transactions on Knowledge and Data Engineering
Logical Calculi for Knowledge Discovery in Databases
PKDD '97 Proceedings of the First European Symposium on Principles of Data Mining and Knowledge Discovery
Data Mining Using Dynamically Constructed Recurrent Fuzzy Neural Networks
PAKDD '98 Proceedings of the Second Pacific-Asia Conference on Research and Development in Knowledge Discovery and Data Mining
Formal Logics of Discovery and Hypothesis Formation by Machine
DS '98 Proceedings of the First International Conference on Discovery Science
Acquiring rule sets as a product of learning in a logical neural architecture
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Extraction of Logical Rules from Data by Means of Piecewise-Linear Neural Networks
DS '02 Proceedings of the 5th International Conference on Discovery Science
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In data mining, artificial neural networks have become one of important competitors of traditional statistical methods. They increase the potential of discovering useful knowledge in data, but only if the differences between both kinds of methods are well understood. Therefore, integrative frameworks are urgently needed. In this paper, a framework based on the calculus of observational logic is presented. Basic concepts of that framework are outlined, and it is explained how generalized quantifiers can be defined in an observational calculus to capture data mining with statistical and ANN-based methods.