Semi-supervised incremental learning

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Authors: Bouchachia, A., Prossegger, M. and Duman, H.

http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5573642

Journal: FUZZ-IEEE

Pages: 1-6

Publisher: IEEE

ISBN: 978-1-4244-6919-2

DOI: 10.1109/FUZZY.2010.5584328

This source preferred by Hamid Bouchachia

This data was imported from Scopus:

Authors: Bouchachia, A., Prossegger, M. and Duman, H.

Journal: 2010 IEEE World Congress on Computational Intelligence, WCCI 2010

ISBN: 9781424469208

DOI: 10.1109/FUZZY.2010.5584328

The paper introduces a hybrid evolving architecture for dealing with incremental learning. It consists of two components: resource allocating neural network (RAN) and growing Gaussian mixture model (GGMM). The architecture is motivated by incrementality on one hand and on the other hand by the possibility to handle unlabeled data along with the labeled one, given that the architecture is dedicated to classification problems. The empirical evaluation shows the efficiency of the proposed hybrid learning architecture. © 2010 IEEE.

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