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Analog VLSI and neural systems
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Self-organization and associative memory: 3rd edition
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Learning internal representations by error propagation
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Neural network learning and expert systems
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Neural-Network-Based Fuzzy Logic Control and Decision System
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Rule-based neural networks for classification and probability estimation
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Logic Minimization Algorithms for VLSI Synthesis
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R-MINI: An Iterative Approach for Generating Minimal Rules from Examples
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Use of Contextual Information for Feature Ranking and Discretization
IEEE Transactions on Knowledge and Data Engineering
Symbolic Interpretation of Artificial Neural Networks
IEEE Transactions on Knowledge and Data Engineering
An integrated approach to fuzzy learning vector quantization and fuzzy c-means clustering
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Supervised fuzzy clustering for rule extraction
IEEE Transactions on Fuzzy Systems
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IEEE Transactions on Neural Networks
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ICAISC '08 Proceedings of the 9th international conference on Artificial Intelligence and Soft Computing
Ensemble for high recognition performance FPGA
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Approximation properties of positive boolean functions
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Switching neural networks: a new connectionist model for classification
WIRN'05 Proceedings of the 16th Italian conference on Neural Nets
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The generation of a set of rules underlying a classification problem is performed by applying a new algorithm called Hamming Clustering (HC). It reconstructs the and-or expression associated with any Boolean function from a training set of samples. The basic kernel of the method is the generation of clusters of input patterns that belong to the same class and are close to each other according to the Hamming distance. Inputs which do not influence the final output are identified, thus automatically reducing the complexity of the final set of rules. The performance of HC has been evaluated through a variety of artificial and real-world benchmarks. In particular, its application in the diagnosis of breast cancer has led to the derivation of a reduced set of rules solving the associated classification problem.