Title :
EVOL: ensembles voting online
Author :
Auda, Gasser ; Kamel, Mohamed
Author_Institution :
Pattern Analysis & Machine Intelligence Lab., Waterloo Univ., Ont., Canada
Abstract :
Cooperation by voting is one of the popular modular neural network decision-making strategies. Ensemble classifiers are multiple identical modules which use voting for post-learning classification. This paper suggests a new cooperation scheme for ensembles which utilizes voting in the learning process itself. According to the suggested scheme, different modules would, automatically, focus on different regions in the input space. Hence, temporal crosstalk decreases and decision boundaries are drawn accurately in complex overlapping regions of the input space
Keywords :
cooperative systems; learning (artificial intelligence); neural nets; pattern classification; real-time systems; cooperation; decision-making; ensemble classifiers; ensembles voting online; modular neural network; post-learning classification; Crosstalk; Decision making; Design engineering; Filtering; Machine intelligence; Multi-layer neural network; Neural networks; Pattern analysis; System analysis and design; Voting;
Conference_Titel :
Neural Networks Proceedings, 1998. IEEE World Congress on Computational Intelligence. The 1998 IEEE International Joint Conference on
Conference_Location :
Anchorage, AK
Print_ISBN :
0-7803-4859-1
DOI :
10.1109/IJCNN.1998.685972
