Robust modular artmap for multi-class shape recognition
Contribuinte(s) |
[Unknown] |
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Data(s) |
01/01/2008
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Resumo |
This paper presents a fuzzy ARTMAP (FAM) based modular architecture for multi-class pattern recognition known as modular adaptive resonance theory map (MARTMAP). The prediction of class membership is made collectively by combining outputs from multiple novelty detectors. Distance-based familiarity discrimination is introduced to improve the robustness of MARTMAP in the presence of noise. The effectiveness of the proposed architecture is analyzed and compared with ARTMAP-FD network, FAM network, and One-Against-One Support Vector Machine (OAO-SVM). Experimental results show that MARTMAP is able to retain effective familiarity discrimination in noisy environment, and yet less sensitive to class imbalance problem as compared to its counterparts.<br /> |
Identificador | |
Idioma(s) |
eng |
Publicador |
IEEE |
Relação |
http://dro.deakin.edu.au/eserv/DU:30048731/lim-robustmodular-2008.pdf http://hdl.handle.net/10.1109/IJCNN.2008.4634132 |
Palavras-Chave | #adaptive resonance theories #class imbalance problems #fuzzy ARTMAP #modular architectures #noisy environments #proposed architectures #shape recognitions #support vectors |
Tipo |
Conference Paper |