A Classification Method Using Nearest Neighbor Method based on The Mean of Norm in Prototypes in Each Class which are Minimum Distance from An Input Pattern and Its k Neighbor Prototypes

Hiroki Imamura; Seiji Hotta; Makoto Fujimura; Hideo Kuroda

doi:10.14864/softscis.2006.0.407.0

SCIS & ISIS 2006

Session ID : TH-G3-3

DOI https://doi.org/10.14864/softscis.2006.0.407.0

Conference information

Host: Japan SOciety for Fuzzy Theory and intelligent informatics

Co-host: The Korea Fuzzy Logic and Intelligent Systems Society, IEEE Computational Intelligence Society, The International Fuzzy Systems Association, 21th Century COE Program "Creation of Agent-Based Social Systems Sciences"

TH-G3 Integrated Soft Computing: Practice and Theory (1)

A Classification Method Using Nearest Neighbor Method based on The Mean of Norm in Prototypes in Each Class which are Minimum Distance from An Input Pattern and Its k Neighbor Prototypes

*Hiroki Imamura, Seiji Hotta, Makoto Fujimura, Hideo Kuroda

Author information

Keywords: Pattern Classification, Nearest Neigbor Method, The Mean of Norm

CONFERENCE PROCEEDINGS FREE ACCESS

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Abstract

In this paper, for accurate classification in arbitrary distributed patterns in sparse distribution, we propose a classification method using nearest neighbor method based on the mean of norm in prototypes in each class which are minimum distance from an input pattern and Its k neighbor prototypes.

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