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Please use this identifier to cite or link to this item: http://hdl.handle.net/10445/5281

Title: Class-Proximity SOM and its Applications in Classification
Authors: Hartono, Pitoyo
Saito, Aya
Abstract: In this study, we propose a model of Self-Organizing Map (SOM) capable of mapping highdimensional data into a low dimension space by preserving not only thefeature-proximity of the original data but also their class-proximity. A conventional SOM is known to map original high dimensional data with similar features into points located close to each other in the low dimensional map in a so called competitive layer. In addition to this feature, the proposed SOM is also able to map high dimensional data belonging to a same class in each other's proximities. These characteristics retains the ability of the map to be used as a visualization tool of high dimensional data while also support the execution of high quality pattern classifications in the low dimensional map. In the experiments the classification performance of the proposed SOM is compared to that of MLP with regards to wide varieties of problems.
Research Achievement Classification: 国際会議/International Conference
Type: Conference Paper
Peer Review: あり/yes
Solo/Joint Author(s): 共著/joint
Published journal or presented
academic conference: 
Proc. IEEE Int. Conf. on Systems, Man and Cybernetics (SMC 2008)
Spage: 2150
Epage: 2155
Date: 2008
Publisher: IEEE
Appears in Collections:Pitoyo, Hartono

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