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An evolutionary approach for the clustering data problem.

, , , и . IJCNN, стр. 1945-1950. IEEE, (2008)

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F -G Pajonk

Harald -F G Möhring

Marco F G Jermini

Miguel Soares

Rodrigo Bienko

 

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On the performance of pairings of activation and loss functions in neural networks., и . IJCNN, стр. 326-333. IEEE, (2016)An Analysis of Meta-learning Techniques for Ranking Clustering Algorithms Applied to Artificial Data., , и . ICANN (1), том 5768 из Lecture Notes in Computer Science, стр. 131-140. Springer, (2009)Comparative study on normalization procedures for cluster analysis of gene expression datasets., , , , , и . IJCNN, стр. 2792-2798. IEEE, (2008)Empirical comparison of Dynamic Classifier Selection methods based on diversity and accuracy for building ensembles., , , и . IJCNN, стр. 1480-1487. IEEE, (2008)Evolving both size and accuracy of RBF networks using Memetic Algorithm., , , и . IJCNN, стр. 1938-1944. IEEE, (2008)An evolutionary approach for the clustering data problem., , , и . IJCNN, стр. 1945-1950. IEEE, (2008)Using Accuracy and Diversity to Select Classifiers to Build Ensembles., , , и . IJCNN, стр. 1310-1316. IEEE, (2006)A Dynamic Classifier Selection Method to Build Ensembles using Accuracy and Diversity., , , и . SBRN, стр. 36-41. IEEE Computer Society, (2006)Métodos de Machine Learning Aplicados no Cenário da Educaçáo a Distância Brasileira (Machine Learning Techniques Applied to the Brazilian Distance Education)., , и . SIIE, том 2733 из CEUR Workshop Proceedings, CEUR-WS.org, (2020)Ranking and selecting clustering algorithms using a meta-learning approach., , , , , , и . IJCNN, стр. 3729-3735. IEEE, (2008)