Information theoretic competitive learning in self-adaptive multi-layered networks |
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Authors: | Ryotaro Kamimura |
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Affiliation: | Joint Research Centre , Tokai University Information Science Laboratory and Future Science and Technology , 1117 Kitakaname Hiratsuka, Kanagawa, Japan , 259-1292 E-mail: ryo@cc.u-tokai.ac.jp |
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Abstract: | In this paper, we propose self-adaptive multi-layered networks in which information in each processing layer is always maximized. Using these multi-layered networks, we can solve complex problems and discover salient features that single-layered networks fail to extract. In addition, this successive information maximization enables networks gradually to extract important features. We applied the new method to the Iris data problem, the vertical-horizontal lines detection problem, a phonological data analysis problem and a medical data problem. Experimental results confirmed that information can repeatedly be maximized in multi-layered networks and that the networks can extract features that cannot be detected by single-layered networks. In addition, features extracted in successive layers are cumulatively combined to detect more macroscopic features. |
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Keywords: | Information Maximization Competitive Learning Multi-layered Networks Feature Extraction Feature Detection |
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