<?xml version="1.0" encoding="UTF-8"?> <article key="pdf/5583" mdate="2011-07-20 00:00:00"> <author>Nazif 脟al谋艧 and Murat Eri艧o臒lu and Hamza Erol and Tayfun Servi</author> <title>Self Organizing Mixture Network in Mixture Discriminant Analysis An Experimental Study</title> <pages>1015 - 1018</pages> <year>2011</year> <volume>5</volume> <number>7</number> <journal>International Journal of Mathematical and Computational Sciences</journal> <ee>https://publications.waset.org/pdf/5583</ee> <url>https://publications.waset.org/vol/55</url> <publisher>World Academy of Science, Engineering and Technology</publisher> <abstract>In the recent works related with mixture discriminant analysis (MDA), expectation and maximization (EM) algorithm is used to estimate parameters of Gaussian mixtures. But, initial values of EM algorithm affect the final parameters estimates. Also, when EM algorithm is applied two times, for the same data set, it can be give different results for the estimate of parameters and this affect the classification accuracy of MDA. Forthcoming this problem, we use Self Organizing Mixture Network (SOMN) algorithm to estimate parameters of Gaussians mixtures in MDA that SOMN is more robust when random the initial values of the parameters are used 5. We show effectiveness of this method on popular simulated waveform datasets and real glass data set.</abstract> <index>Open Science Index 55, 2011</index> </article>