Abstract
In this paper, we develop a new multilayer neural model that forms categories of inputs for some practical applications such as pattern recognition, learning, image processing, and trend analysis. An essential core of the model is to use a novel vector representation of concepts that compose an input in a multi-level informational hierarchy that makes the model possess category formation ability from incomplete observation of the input. 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. In addition, we evaluate the adequacy and efficiency of formed categories by using principal component analysis.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the SICE Annual Conference |
| Pages | 2895-2900 |
| Number of pages | 6 |
| Publication status | Published - 2005 |
| Event | SICE Annual Conference 2005 - Okayama, Japan Duration: 2005 Aug 8 → 2005 Aug 10 |
Other
| Other | SICE Annual Conference 2005 |
|---|---|
| Country/Territory | Japan |
| City | Okayama |
| Period | 05/8/8 → 05/8/10 |
Keywords
- Concept formation
- Hebbian rule
- Incremental learning
- Neural networks
- Pattern recognition
- Principal component analysis
- Self-organization
ASJC Scopus subject areas
- Engineering(all)