Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
Instance-Based Learning Algorithms
Machine Learning
C4.5: programs for machine learning
C4.5: programs for machine learning
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Generating Accurate Rule Sets Without Global Optimization
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Probabilistic neural-network structure determination for pattern classification
IEEE Transactions on Neural Networks
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The design of nearest neighbour classifiers is very dependent from some crucial parameters involved in learning, like the number of prototypes to use, the initial localization of these prototypes, and a smoothing parameter. These parameters have to be found by a trial and error process or by some automatic methods. In this work, an evolutionary approach based on Nearest Neighbour Classifier (ENNC), is described. Main property of this algorithm is that it does not require any of the above mentioned parameters. The algorithm is based on the evolution of a set of prototypes that can execute several operators in order to increase their quality in a local sense, and emerging a high classification accuracy for the whole classifier.