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dc.contributor.authorLin, Chin-Tengen_US
dc.contributor.authorLi, Dong-Linen_US
dc.contributor.authorChang, Jyh-Yeongen_US
dc.date.accessioned2014-12-08T15:24:35Z-
dc.date.available2014-12-08T15:24:35Z-
dc.date.issued2011en_US
dc.identifier.isbn978-3-642-24964-8en_US
dc.identifier.issn0302-9743en_US
dc.identifier.urihttp://hdl.handle.net/11536/17052-
dc.description.abstractIn this paper, we propose a novel artificial neural network, called self-adjusting feature map (SAM), and its unsupervised learning algorithm with self-adjusting mechanism. After the training of SAM network, we will obtain a map composed of a set of representative connected neurons. The trained network structure of representative connected neurons not only displays the spatial relation of the input data distribution but also quantizes the data well. SAM can automatically isolate a set of connected neurons, in which the number of the set may indicate the number of clusters to be used. The idea of self-adjusting mechanism is based on combining of mathematical statistics and neurological advance and retreat of waste. For each representative neuron, there are three periods, growth. adaptation and decline, in its training process. The network of representative neurons will first create the necessary neurons according to the local density of the input data in the growth period. Then it will adjust neighborhood neuron pair's connected/disconnected topology constantly according to the statistics of input feature data in the adaptation period. Lastly the unnecessary neurons of the network will be merged or deleted in the decline period. In this study. we exploit SAM to handle some peculiar cases that cannot be well dealt with by classical unsupervised learning networks such as self-organizing feature map (SOM) network. Furthermore, we also take several real world cases to exhibit the remarkable characteristics of SAM.en_US
dc.language.isoen_USen_US
dc.subjectUnsupervised learningen_US
dc.subjectself-adjustingen_US
dc.subjectrepresentative neurons statisticsen_US
dc.subjectSOMen_US
dc.titleSelf-Adjusting Feature Maps Networken_US
dc.typeProceedings Paperen_US
dc.identifier.journalNEURAL INFORMATION PROCESSING, PT IIIen_US
dc.citation.volume7064en_US
dc.citation.spage356en_US
dc.citation.epage364en_US
dc.contributor.department電控工程研究所zh_TW
dc.contributor.departmentInstitute of Electrical and Control Engineeringen_US
dc.identifier.wosnumberWOS:000307328500040-
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