DocumentCode :
1685859
Title :
Learn++: a classifier independent incremental learning algorithm for supervised neural networks
Author :
Polikar, Robi ; Byorick, Jeff ; Krause, Stefan ; Marino, Anthony ; Moreton, Michael
Author_Institution :
Electr. & Comput. Eng., Rowan Univ., Glassboro, NJ, USA
Volume :
2
fYear :
2002
fDate :
6/24/1905 12:00:00 AM
Firstpage :
1742
Lastpage :
1747
Abstract :
A versatile incremental learning algorithm is introduced for supervised neural network type classifiers. The proposed algorithm, called Learn++, exploits the synergistic expressive power of an ensemble of weak classifiers for learning additional information from new data. Learn++ is capable of learning new classes, without forgetting previously acquired knowledge, even when the previously used data is no longer available. Furthermore, Learn++ is independent of the specific type of the classifier, and adds the incremental learning capability to any supervised neural network classifier
Keywords :
learning (artificial intelligence); neural nets; Learn++; classifier independent incremental learning algorithm; supervised neural network classifier; supervised neural networks; synergistic expressive power; weak classifiers; Availability; Computer networks; Function approximation; Inference algorithms; Machine learning; Neural networks; Pattern recognition; Power engineering and energy; Power engineering computing; Stability;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks, 2002. IJCNN '02. Proceedings of the 2002 International Joint Conference on
Conference_Location :
Honolulu, HI
ISSN :
1098-7576
Print_ISBN :
0-7803-7278-6
Type :
conf
DOI :
10.1109/IJCNN.2002.1007781
Filename :
1007781
Link To Document :
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