Self-organizing incremental neural network and its application

Authors:
Furao Shen;Osamu Hasegawa
Affiliations:
National Key Laboratory for Novel Software Technology, Nanjing University, China and Jiangyin Information Technology Research Institute, Nanjing University, China;Imaging Science and Engineering Lab., Tokyo Institute of Technology, Japan
Venue:
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part III
Year:
2010

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Abstract

Self-organizing incremental neural network (SOINN) is introduced. SOINN is able to represent the topology structure of input data, incrementally learn new knowledge without destroy of learned knowledge, and process online non-stationary data. It is free of prior conditions such as a suitable network structure or network size, and it is also robust to noise. SOINN has been adapted for unsupervised learning, supervised learning, semi-supervised learning, and active learning tasks. Also, SOINN is used for some applications such as associative memory, pattern-based reasoning, word-grounding, gesture recognition, and robotics.