Overview of some incremental learning algorithms
Authors: Bouchachia, A., Gabrys, B. and Sahel, Z.
Journal: IEEE International Conference on Fuzzy Systems
ISBN: 9781424412105
ISSN: 1098-7584
DOI: 10.1109/FUZZY.2007.4295640
Abstract:Incremental learning (IL) plays a key role in many real-world applications where data arrives over time. It is mainly concerned with learning models in an everchanging environment. In this paper, we review some of the incremental learning algorithms and evaluate them within the same experimental settings in order to provide as objective comparative study as possible. These algorithms include fuzzy ARTMAP, nearest generalized exemplar, growing neural gas, generalized fuzzy min-max neural network, and IL based on function decomposition (ILFD). © 2007 IEEE.
Source: Scopus
Overview of Some Incremental Learning Algorithms
Authors: Bouchachia, A., Gabrys, B. and Sahel, Z.
Conference: IEEE International Conference on Fuzzy Systems (FUZZ-IEEE'2007): Proceedings
Dates: 23-26 July 2007
Pages: 1-6
Publisher: London, UK, July 2007
ISSN: 1098-7584
DOI: 10.1109/FUZZY.2007.4295640
Abstract:Incremental learning (IL) plays a key role in many real-world applications where data arrives over time. It is mainly concerned with learning models in an ever-changing environment. In this paper, we review some of the incremental learning algorithms and evaluate them within the same experimental settings in order to provide as objective comparative study as possible. These algorithms include fuzzy ARTMAP, nearest generalized exemplar, growing neural gas, generalized fuzzy min-max neural network, and IL based on function decomposition (ILFD).
Source: Manual
Preferred by: Hamid Bouchachia
Overview of Some Incremental Learning Algorithms.
Authors: Bouchachia, A., Gabrys, B. and Sahel, Z.
Journal: FUZZ-IEEE
Pages: 1-6
Publisher: IEEE
http://www.informatik.uni-trier.de/~ley/db/conf/fuzzIEEE/fuzzIEEE2007.html
Source: DBLP