Abstract
Based on unsupervised learning paradigm, self-organizing neural networks have achieved great success in applications of automatic pattern discovery. However, the development of self-organizing neural networks is traditionally based on the assumption that data are governed by a normal distribution. Application of self-organizing neural networks in the areas where data are better modelled by other statistical distributions such as a Poisson distribution has received less attention. Based on the incorporation of the statistical nature of data with a Poisson distribution into a Self-Organizing Map, this paper presents a Poisson-based self-organizing neural network. The proposed network has been tested on two datasets including a real biological example. The results indicate that, in comparison to traditional self-organizing maps, the proposed model offers substantial improvements in pattern discovery in data governed by a Poisson distribution.
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Wang, H., Zheng, H. (2008). Poisson-Based Self-Organizing Neural Networks for Pattern Discovery. In: Huang, DS., Wunsch, D.C., Levine, D.S., Jo, KH. (eds) Advanced Intelligent Computing Theories and Applications. With Aspects of Theoretical and Methodological Issues. ICIC 2008. Lecture Notes in Computer Science, vol 5226. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87442-3_21
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DOI: https://doi.org/10.1007/978-3-540-87442-3_21
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