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 |
|---|---|
| Pages (from-to) | 195-206 |
| Number of pages | 12 |
| Journal | Applied Artificial Intelligence |
| Volume | 6 |
| Issue number | 2 |
| State | Published - Apr 1 1992 |
| Externally published | Yes |
Fingerprint
ASJC Scopus subject areas
- Artificial Intelligence
- Control and Systems Engineering
- Electrical and Electronic Engineering
Cite this
Forgetting and aging of knowledge in concept formation. / Kubat, Miroslav; Krizakova, Ivana.
In: Applied Artificial Intelligence, Vol. 6, No. 2, 01.04.1992, p. 195-206.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Forgetting and aging of knowledge in concept formation
AU - Kubat, Miroslav
AU - Krizakova, Ivana
PY - 1992/4/1
Y1 - 1992/4/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=0026844245&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=0026844245&partnerID=8YFLogxK
M3 - Article
AN - SCOPUS:0026844245
VL - 6
SP - 195
EP - 206
JO - Applied Artificial Intelligence
JF - Applied Artificial Intelligence
SN - 0883-9514
IS - 2
ER -