Automatic' learning strategies and their application to electrophoresis analysis

Article Article scientifique Article Automatic' learning strategies and their application to electrophoresis analysis eng 1989 author Roch Christian Maurice 32039 author Pun Thierry 22776 author Hochstrasser Denis 26422 author Pellegrini Christian 24713 383-391 10.1016/0895-6111(89)90225-5 0895-6111 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. Image analysis Pattern recognition Automatic learning Artificial intelligence Expert system Conceptual clustering Two-dimensional gel electrophoresis Dewey 025.063 13 Centre universitaire d'informatique 0D3 32 Département d'informatique 18 false 895 Computer Vision and Multimedia Laboratory 725 Multimodal Interaction Group Computerized medical imaging and graphics 13 5 b1051590-724d-490b-a4d2-8b4d8733c3d1 Article (Published version) Public 1989Roch_automatic_learning.pdf 1135382 application/pdf openAccess Centres et instituts / Centre universitaire d'informatique Faculté des sciences / Département d'informatique

Title	Published in	Access level	OA Policy	Year	Views	Downloads

Automatic' learning strategies and their application to electrophoresis analysis	Computerized medical imaging and graphics			1989	585	292

Archive ouverte UNIGE

Roch, Christian Maurice