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- ELSEVIER European Journal of Operational Research 108 (1998) 283-292 OF OPERATIONAL
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- 966 IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. 17, NO. 4, JULY 2006 An Incremental Training Method for the
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- Computer Physics Communications 135 (2001) 167175 www.elsevier.nl/locate/cpc
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- Information LettersELSEVIER Information Processing Letters 53 (1995) 229-234
- IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. 18, NO. 3, MAY 2007 745 Unsupervised Learning of Gaussian Mixtures Based
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- IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS--PART A: SYSTEMS AND HUMANS, VOL. 29, NO. 4, JULY 1999 393 ACKNOWLEDGMENT
- Computer Physics Communications
- IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 18, NO. 4, APRIL 2009 753 Variational Bayesian Sparse Kernel-Based Blind
- IEEE TRANSACTIONS ON MEDICAL IMAGING, VOL. ??, NO. ??, ?? 2005 1 Mixture Model Analysis of DNA Microarray
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- IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 17, NO. 10, OCTOBER 2008 1795 Variational Bayesian Image Restoration Based on a
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- Chaoc, Solams & Fracrals Vol. 5, No. 5. pp. 139-746. 1995 Copyright 0 1995 Elsevier Science Ltd
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- Cytological Diagnosis Based on Fuzzy Neural K. Blekas and A. Stafylopatis \Lambda , D. Kontoravdis y , A. Likas z ,
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- Applied Intelligence 7, 215225 (1997) fl1997 Kluwer Academic Publishers
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