Overview of some incremental learning algorithms

This source preferred by Hamid Bouchachia

Authors: Bouchachia, A., Gabrys, B. and Sahel, Z.

Start date: 23 July 2007

Pages: 1-6

Publisher: London, UK, July 2007

ISSN: 1098-7584

DOI: 10.1109/FUZZY.2007.4295640

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).

This data was imported from DBLP:

Authors: Bouchachia, A., Gabrys, B. and Sahel, Z.

http://www.informatik.uni-trier.de/~ley/db/conf/fuzzIEEE/fuzzIEEE2007.html

Journal: FUZZ-IEEE

Pages: 1-6

Publisher: IEEE

DOI: 10.1109/FUZZY.2007.4295640

This data was imported from Scopus:

Authors: Bouchachia, A., Gabrys, B. and Sahel, Z.

Journal: IEEE International Conference on Fuzzy Systems

ISSN: 1098-7584

DOI: 10.1109/FUZZY.2007.4295640

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.

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