Abstract
One of the problems solved by machine learning based techniques is symbolic data analysis and concept formation. We report on the program FAVORIT, which achieves a performance improvement over its predecessors (such as UNIMEM) by means of a simple mechanism mimicking the shortcomings of human learning: aging of knowledge and forgetting. When applied to large and noisy data sets, these characteristics enable efficient restructuring and pruning of the internal knowledge structures. The paper contains a brief description of the program, together with the rationale behind its philosophy, as well as a simple case study.
Original language | English (US) |
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Pages (from-to) | 195-206 |
Number of pages | 12 |
Journal | Applied Artificial Intelligence |
Volume | 6 |
Issue number | 2 |
DOIs | |
State | Published - Jan 1 1992 |
Externally published | Yes |
ASJC Scopus subject areas
- Artificial Intelligence