Title :
Semi-supervised incremental learning
Author :
Bouchachia, Abdelhamid ; Prossegger, Markus ; Duman, Hakan
Author_Institution :
Dept. of Inf., Univ. of Klagenfurt, Klagenfurt, Austria
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.
Keywords :
Gaussian processes; data handling; learning (artificial intelligence); neural nets; pattern classification; resource allocation; growing Gaussian mixture model; hybrid learning architecture; resource allocating neural network; semisupervised incremental learning; unlabeled data handling; Accuracy; Computational modeling; Computer architecture; Covariance matrix; Data models; Machine learning; Radio access networks;
Conference_Titel :
Fuzzy Systems (FUZZ), 2010 IEEE International Conference on
Conference_Location :
Barcelona
Print_ISBN :
978-1-4244-6919-2
DOI :
10.1109/FUZZY.2010.5584328
