作成者 |
|
|
|
本文言語 |
|
発行日 |
|
収録物名 |
|
開始ページ |
|
終了ページ |
|
出版タイプ |
|
アクセス権 |
|
関連DOI |
|
|
|
|
関連URI |
|
|
|
|
関連情報 |
|
|
|
|
概要 |
In this paper, we propose a recognition method of hand shapes using Hyper-Column Model (HCM). HCM is a model to recognize images, and consists of Hierarchical Self-Organizing Maps (HSOM) and Neocognit...ron (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 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.続きを見る
|