Supervised learning in Hyper-Column Model

Research output: Contribution to conferencePaperpeer-review

Abstract

In this paper, we propose a supervised learning method in Hyper-Column Model (HCM). HCM is a model to recognize images, and consists of Hierarchical Self-Organizing Maps (HSOM) and Neocognitron (NC). HCM complements disadvantages of HSOM and NC, and inherits advantages from them. There is a problem, however, that HCM does not suit general image recognition in HCM since its learning method is an unsupervised one with competitive learning which is used by Self-Organizing Map (SOM). Therefore, we extended HCM to a supervised learnable model with an associative memory of SOM. We have found that an ability of HCM with supervised learning is superior to the one with unsupervised learning.

Original languageEnglish
Pages481-488
Number of pages8
Publication statusPublished - 2005
Event5th Workshop on Self-Organizing Maps, WSOM 2005 - Paris, France
Duration: Sep 5 2005Sep 8 2005

Other

Other5th Workshop on Self-Organizing Maps, WSOM 2005
Country/TerritoryFrance
CityParis
Period9/5/059/8/05

All Science Journal Classification (ASJC) codes

  • Information Systems

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