| 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 an associative 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 learning of SOM. We have found that an ability of HCM with the associative learning is superior to the one with unsupervised learning.続きを見る
|
Mendeley出力
ShimadaFCV06