Combining Symbolic and Neural Learning
Machine Learning
Using neural networks for data mining
Future Generation Computer Systems - Special double issue on data mining
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Beyond Simple Rule Extraction: The Extraction of Planning Knowledge from Reinforcement Learners
IJCNN '00 Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 2 - Volume 2
Fast learning in networks of locally-tuned processing units
Neural Computation
Rule Extraction from Radial Basis Function Networks by Using Support Vectors
IBERAMIA 2002 Proceedings of the 8th Ibero-American Conference on AI: Advances in Artificial Intelligence
Feature Extraction Based on ICA for Binary Classification Problems
IEEE Transactions on Knowledge and Data Engineering
Rule-Based Learning Systems for Support Vector Machines
Neural Processing Letters
Training without data: knowledge insertion into RBF neural networks
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
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Extracting rules from RBFs is not a trivial task because of nonlinear functions or high input dimensionality. In such cases, some of the hidden units of the RBF network have a tendency to be "shared" across several output classes or even may not contribute to any output class. To address this we have developed an algorithm called LREX (for Local Rule EXtraction) which tackles these issues by extracting rules at two levels: hREX extracts rules by examining the hidden unit to class assignments while mREX extracts rules based on the input space to output space mappings. The rules extracted by our algorithm are compared and contrasted against a competing local rule extraction system. The central claim of this paper is that local function networks such as radial basis function (RBF) networks have a suitable architecture based on Gaussian functions that is amenable to rule extraction.