Difference between revisions of "Publications:Almost Linear Biobasis Function Neural Networks"

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|Name=You, Liwen [yoli] (Högskolan i Halmstad [2804], Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE) [3905], Halmstad Embedded and Intelligent Systems Research (EIS) [3938], Intelligenta system (IS-lab) [3941]);Rögnvaldsson, Thorsteinn [denni] (Högskolan i Halmstad [2804], Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE) [3905], Halmstad Embedded and Intelligent Systems Research (EIS) [3938], Intelligenta system (IS-lab) [3941])
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|Name=You, Liwen (liyo) (Högskolan i Halmstad (2804), Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE) (3905), Halmstad Embedded and Intelligent Systems Research (EIS) (3938), Intelligenta system (IS-lab) (3941));Rögnvaldsson, Thorsteinn (denni) (0000-0001-5163-2997) (Högskolan i Halmstad (2804), Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE) (3905), Halmstad Embedded and Intelligent Systems Research (EIS) (3938), Intelligenta system (IS-lab) (3941))
 
|Title=Almost Linear Biobasis Function Neural Networks
 
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Latest revision as of 21:40, 30 September 2016

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Title Almost Linear Biobasis Function Neural Networks
Author Liwen You and Thorsteinn Rögnvaldsson
Year 2007
PublicationType Conference Paper
Journal
HostPublication The 2007 International Joint Conference on Neural Networks : IJCNN 2007 conference proceedings : August 12-17, 2007, Resaissance Orlando Resort, Orlando, Florida, USA
Conference The 2007 International Joint Conference on Neural Networks
DOI http://dx.doi.org/10.1109/IJCNN.2007.4371226
Diva url http://hh.diva-portal.org/smash/record.jsf?searchId=1&pid=diva2:327085
Abstract An analysis of biobasis function neural networks is presented, which shows that the similarity metric used is a linear function and that bio-basis function neural networks therefore often end up being just linear classifiers in high dimensional spaces. This is a consequence of four things: the linearity of the distance measure, the normalization of the distance measure, the recommended default values of the parameters, and that biological data sets are sparse.