Semi-supervised incremental learning
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
Abstract: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.
Source: Scopus
Preferred by: Hamid Bouchachia
Semi-Supervised Incremental Learning
Authors: Bouchachia, A., Prossegger, M. and Duman, H.
Journal: 2010 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ-IEEE 2010)
ISSN: 1098-7584
Source: Web of Science (Lite)
Semi-supervised incremental learning.
Authors: Bouchachia, A., Prossegger, M. and Duman, H.
Journal: FUZZ-IEEE
Pages: 1-6
Publisher: IEEE
ISBN: 978-1-4244-6919-2
http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5573642
Source: DBLP