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Evolving clustering, classification and regression with TEDA
2015
2015 International Joint Conference on Neural Networks (IJCNN)
In th i s art i cle the novel cluster i ng and regress i on methods TEDACluster and TEDAPredict methods are descr i bed additionally to recently proposed evolv i ng class i fier TEDAClass. The algor i thms for class i fication, cluster i ng and regression are based on the recently proposed AnYa type fuzzy rule based system. The novel methods use the recently proposed TEDA framework capable of recursive process i ng of large amounts of data. The framework i s capable of computationally cheap
doi:10.1109/ijcnn.2015.7280528
dblp:conf/ijcnn/KanginA15
fatcat:by7zsla2krdqxjs6j2xbqudi2u