A Nearest Hyperrectangle Learning Method
Machine Learning
Universal approximation using radial-basis-function networks
Neural Computation
Machine Learning
Unifying instance-based and rule-based induction
Machine Learning
Using neural networks for data mining
Future Generation Computer Systems - Special double issue on data mining
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Hybrid Neural Systems, revised papers from a workshop
Fast learning in networks of locally-tuned processing units
Neural Computation
Knowledge extraction from local function networks
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
IEEE Transactions on Neural Networks
Data mining in soft computing framework: a survey
IEEE Transactions on Neural Networks
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In this paper, a procedure for rule extraction from radial basis function networks (RBFNs) is proposed. The algorithm is based on the use of a support vector machine (SVM) as a frontier pattern selector. By using geometric methods, centers of the RBF units are combined with support vectors in order to construct regions (ellipsoids or hyper-rectangles) in the input space, which are later translated to if-then rules. Additionally, the support vectors are used to determine overlapping between classes and to refine the rule base. The experimental results indicate that a very high fidelity between RBF network and the extracted set of rules can be achieved with low overlapping between classes.