Article (Published version) (1.1 MB) - 
Free access
Highlights
Scientific Article
unige:47497 
 |
Automatic' learning strategies and their application to electrophoresis analysis |
|
| Authors | ||
| Published in | Computerized Medical Imaging and Graphics. 1989, vol. 13, no. 5, p. 383-391 | |
| Abstract | Automatic learning plays an important role in image analysis and pattern recognition. A taxonomy of automatic learning strategies is presented; this categorization is based on the amount of inferences the learning element must perform to bridge the gap between environmental and system knowledge representation level. Four main categories are identified and described: rote learning, learning by deduction, learning by induction, and learning by analogy. An application of learning by induction to medical image analysis is then exposed. It consists in the classification of two-dimensional gel electrophoretograms into meaningful distinct classes, as well in their conceptual description. | |
| Keywords | Image analysis — Pattern recognition — Automatic learning — Artificial intelligence — Expert system — Conceptual clustering — Two-dimensional gel electrophoresis | |
| Identifiers | ||
| Full text | ||
| Structures | ||
| Research groups | Computer Vision and Multimedia Laboratory Multimodal Interaction Group | |
| Citation (ISO format) | ROCH, Christian Maurice et al. Automatic' learning strategies and their application to electrophoresis analysis. In: Computerized Medical Imaging and Graphics, 1989, vol. 13, n° 5, p. 383-391. doi: 10.1016/0895-6111(89)90225-5 https://archive-ouverte.unige.ch/unige:47497 |
361 hits 
131 downloads 
Contact an author
Update