TY - GEN
T1 - An autonomous learning algorithm of resource allocating network
AU - Tabuchi, Toshihisa
AU - Ozawa, Seiichi
AU - Roy, Asim
N1 - Funding Information:
The authors would like to thank Professor Shigeo Abe for his helpful comments and discussions. This research was partially supported by the Ministry of Education, Science, Sports and Culture, Grant-in-Aid for Scientific Research (C) 205002205.
PY - 2009
Y1 - 2009
N2 - Selecting proper parameters of RBF networks has been a puzzling problem even for batch learning. The parameter selection is usually carried out by an external supervisor. To exclude the intervention by an external supervisor from the parameter selection, we propose a new learning scheme called Autonomous Learning algorithm for Resource Allocating Network (AL-RAN). AL-RAN is an incremental learning algorithm which consists of the following functions: automated data normalization and automated adjustment of RBF widths. In the experiments, we evaluate AL-RAN using nine benchmark datasets in terms of the decision accuracy of data normalization and the final classification accuracy. The experimental results demonstrate that the above two functions in AL-RAN work well and the final classification accuracy of AL-RAN is almost the same as that of a non-autonomous model whose parameters are manually tuned by an external supervisor.
AB - Selecting proper parameters of RBF networks has been a puzzling problem even for batch learning. The parameter selection is usually carried out by an external supervisor. To exclude the intervention by an external supervisor from the parameter selection, we propose a new learning scheme called Autonomous Learning algorithm for Resource Allocating Network (AL-RAN). AL-RAN is an incremental learning algorithm which consists of the following functions: automated data normalization and automated adjustment of RBF widths. In the experiments, we evaluate AL-RAN using nine benchmark datasets in terms of the decision accuracy of data normalization and the final classification accuracy. The experimental results demonstrate that the above two functions in AL-RAN work well and the final classification accuracy of AL-RAN is almost the same as that of a non-autonomous model whose parameters are manually tuned by an external supervisor.
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U2 - 10.1007/978-3-642-04394-9_17
DO - 10.1007/978-3-642-04394-9_17
M3 - Conference contribution
AN - SCOPUS:76249112616
SN - 3642043933
SN - 9783642043932
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 134
EP - 141
BT - Intelligent Data Engineering and Automated Learning - IDEAL 2009 - 10th International Conference, Proceedings
T2 - 10th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2009
Y2 - 23 September 2009 through 26 September 2009
ER -