In this paper, we develop a new neural model that deals with continuation value of inputs for some practical applications of pattern recognition task. An essential core of the model is use of a novel vector representation of a target concept in a multi-level informational hierarchy that makes the model possess category formation ability from incomplete observation of the target. Simulation results demonstrate the usefulness of the model for a facial image recognition task, even if it is carried out under an incremental and unsupervised learning environment.
|ホスト出版物のタイトル||Proceedings of the SICE Annual Conference|
|出版ステータス||Published - 2004|
|イベント||SICE Annual Conference 2004 - Sapporo, Japan|
継続期間: 2004 8月 4 → 2004 8月 6
|Other||SICE Annual Conference 2004|
|Period||04/8/4 → 04/8/6|
ASJC Scopus subject areas