Abstract
Self Organizing Map (SOM) is a kind of neural networks, that learns the feature of input data thorough unsupervised and competitive neighborhood learning. In SOM learning algorithm, every connection weights in SOM feature map are initialized to random values to covers whole space of input data, however, this is also set nodes to random point of SOM feature map independently with data space. The move distance of output nodes increases and learning convergence becomes slow for this. To improve SOM learning speed, here I propose a new method, node exchange of initial SOM feature map, and a new measure of convergence, the average of the move distance of nodes. As a result of experiments, the average of the move distance of nodes comes to short that it becomes about 45%, and learning speed is improved that it becomes about 50% by this method.
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References
Kohonen, T.: Self-Organizing Maps. Springer, Heidelberg (1995) ISBN 3540586008
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Miyoshi, T., Kawai, H., Masuyama, H.: Efficient SOM Learning by Data Order Adjustment. In: Proceedings of 2002 IEEE World Congress on Computational Intelligence (WCCI 2002), IJCNN 2002, USA, pp. 784–784 (2002) ISBN 0-7803-7281-6
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Miyoshi, T. (2005). Node Exchange for Improvement of SOM Learning. In: Khosla, R., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2005. Lecture Notes in Computer Science(), vol 3683. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11553939_81
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DOI: https://doi.org/10.1007/11553939_81
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28896-1
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