Please use this identifier to cite or link to this item: https://doi.org/10.1007/978-3-642-15810-0_36
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dc.titleA comparison between growing and variably dense self organizing maps for incremental learning in Hubel Weisel models of concept representation
dc.contributor.authorDaniel, N.C.K.
dc.contributor.authorRamanathan, K.
dc.contributor.authorLuping, S.
dc.contributor.authorVadakkepat, P.
dc.date.accessioned2014-06-19T02:52:44Z
dc.date.available2014-06-19T02:52:44Z
dc.date.issued2010
dc.identifier.citationDaniel, N.C.K.,Ramanathan, K.,Luping, S.,Vadakkepat, P. (2010). A comparison between growing and variably dense self organizing maps for incremental learning in Hubel Weisel models of concept representation. Communications in Computer and Information Science 103 CCIS : 282-289. ScholarBank@NUS Repository. <a href="https://doi.org/10.1007/978-3-642-15810-0_36" target="_blank">https://doi.org/10.1007/978-3-642-15810-0_36</a>
dc.identifier.isbn3642158099
dc.identifier.issn18650929
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/68745
dc.description.abstractHubel Weisel models of pattern recognition are thought to be anatomically and physiologically faithful models of information representation in the cortex. They describe sensory information as being encoded in a hierarchy of increasingly sophisticated representations across the layers of the cortex. They have been shown in previous studies as robust models of object recognition. In a previous work, we have also shown Hubel Weisel models as being capable of representing a hierarchy of concepts. In this paper, we explore incremental learning with respect to Hubel Weisel models of concept representation. The challenges of incremental learning in the Hubel Weisel model are discussed. We then compare the use of variably dense self organizing maps to perform incremental learning against the original implementation using growing self organizing maps. The use of variable density self organizing maps shows better results in terms of the percentage of documents correctly clustered. The percentage improvement in clustering accuracy is in some cases up to 50% over the original GSOM implementation for the incrementally learnt module. However, we also highlight the issues that make the evaluation of such a model a challenging one. © 2010 Springer-Verlag.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1007/978-3-642-15810-0_36
dc.sourceScopus
dc.subjectCognitive models
dc.subjectConcept representation
dc.subjectHubel Weisel Architectures
dc.subjectSelf Organizing maps
dc.typeConference Paper
dc.contributor.departmentELECTRICAL & COMPUTER ENGINEERING
dc.description.doi10.1007/978-3-642-15810-0_36
dc.description.sourcetitleCommunications in Computer and Information Science
dc.description.volume103 CCIS
dc.description.page282-289
dc.identifier.isiutNOT_IN_WOS
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