An Investigation of the Effects of Variable Vigilance within the RePART Neuro-Fuzzy Network
Journal of Intelligent and Robotic Systems
Knowledge Incorporation and Rule Extraction in Neural Networks
ICANN '01 Proceedings of the International Conference on Artificial Neural Networks
Predictive Self-Organizing Networks for Text Categorization
PAKDD '01 Proceedings of the 5th Pacific-Asia Conference on Knowledge Discovery and Data Mining
Learning Logic Programs with Neural Networks
ILP '01 Proceedings of the 11th International Conference on Inductive Logic Programming
Predictive neural networks for gene expression data analysis
Neural Networks
Towards personalised web intelligence
Knowledge and Information Systems
Machine learning methods for Chinese web page categorization
CLPW '00 Proceedings of the second workshop on Chinese language processing: held in conjunction with the 38th Annual Meeting of the Association for Computational Linguistics - Volume 12
Fuzzy lattice reasoning (FLR) classifier and its application for ambient ozone estimation
International Journal of Approximate Reasoning
Intelligence Through Interaction: Towards a Unified Theory for Learning
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks
Neural logic network learning using genetic programming
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Unsupervised image retrieval framework based on rule base system
Expert Systems with Applications: An International Journal
Supporting image retrieval framework with rule base system
Knowledge-Based Systems
On the design of a multimodal cognitive architecture for perceptual learning in industrial robots
MICAI'05 Proceedings of the 4th Mexican international conference on Advances in Artificial Intelligence
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This paper introduces a hybrid system termed cascade adaptive resonance theory mapping (ARTMAP) that incorporates symbolic knowledge into neural-network learning and recognition. Cascade ARTMAP, a generalization of fuzzy ARTMAP, represents intermediate attributes and rule cascades of rule-based knowledge explicitly and performs multistep inferencing. A rule insertion algorithm translates if-then symbolic rules into cascade ARTMAP architecture. Besides that initializing networks with prior knowledge can improve predictive accuracy and learning efficiency, the inserted symbolic knowledge can be refined and enhanced by the cascade ARTMAP learning algorithm. By preserving symbolic rule form during learning, the rules extracted from cascade ARTMAP can be compared directly with the originally inserted rules. Simulations on an animal identification problem indicate that a priori symbolic knowledge always improves system performance, especially with a small training set. Benchmark study on a DNA promoter recognition problem shows that with the added advantage of fast learning, cascade ARTMAP rule insertion and refinement algorithms produce performance superior to those of other machine learning systems and an alternative hybrid system known as knowledge-based artificial neural network (KBANN). Also, the rules extracted from cascade ARTMAP are more accurate and much cleaner than the NofM rules extracted from KBANN.