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Composition of Learning Patterns Using Spherical Self-Organizing Maps in Image Analysis with Subspace Classifier

  • Nobuo Matsuda
  • Fumiaki Tajima
  • Hedeaki Sato
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 295)

Abstract

This paper describes a composition of learning patterns based on the visualization of a Spherical SOM for improving the performance of image analysis using the Subspace classifier. We have applied the Subspace classifier to image analysis because it has fewer parameters and higher performance. Then we have experienced that the selection of features and learning patterns influence greatly the classification performance through examinations. The Spherical SOM has no border in the array of nodes and eliminates the Border effect problem. Comparing the performance of the image analysis, we show that visualization of the Spherical SOM allows the composition of learning patterns to improve more performance and degree of its reliability than those without the composition.

Keywords

Subspace Classifier Spherical Self-Organizing Map Learning Pattern Fundus Image Visualization 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Dept. Electr. and Mech. Eng.Oshima National College of Maritime TechnologyOshima-gunJapan
  2. 2.Education and Human ScienceYokohama National UniversityYokohamaJapan
  3. 3.Federation of National Public Service Personnel Mutual Aid AssociationTokyoJapan

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