DocumentCode :
2064841
Title :
Incremental learning in autonomous systems: evolving connectionist systems for on-line image and speech recognition
Author :
Kasabov, Nikola ; Zhang, David ; Pang, P.S.
Author_Institution :
Inst. of Knowledge Eng. & Discovery Res., Auckland Univ. of Technol., New Zealand
fYear :
2005
fDate :
12-15 June 2005
Firstpage :
120
Lastpage :
125
Abstract :
The paper presents an integrated approach to incremental learning in autonomous systems, that includes both pattern recognition and feature selection. The approach utilizes evolving connectionist systems (ECoS) and is applied on on-line image and speech pattern learning and recognition tasks. The experiments show that ECoS are a suitable paradigm for building autonomous systems for learning and navigation in a new environment using both image and speech modalities.
Keywords :
feature extraction; image recognition; learning (artificial intelligence); speech recognition; adaptive systems; autonomous systems; evolving connectionist systems; evolving growing cluster classifier; feature selection; incremental learning; multimodal systems; online image recognition; online speech recognition; speech pattern learning; Carbon capture and storage; Clustering algorithms; Computational intelligence; Image recognition; Intelligent robots; Knowledge engineering; Neural networks; Pattern recognition; Speech recognition; Streaming media;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Advanced Robotics and its Social Impacts, 2005. IEEE Workshop on
Print_ISBN :
0-7803-8947-6
Type :
conf
DOI :
10.1109/ARSO.2005.1511636
Filename :
1511636
Link To Document :
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