FAVORIT: Concept formation with ageing of knowledge

Ivana Krizakova; Miroslav Kubat

doi:10.1016/0167-8655(92)90110-L

FAVORIT: Concept formation with ageing of knowledge

Ivana Krizakova, Miroslav Kubat

Research output: Contribution to journal › Article › peer-review

3 Scopus citations

Abstract

Concept formation is a machine-learning based approach to cluster analysis of symbolic data. So far, several techniques have been proposed. The system FAVORIT, developed in our laboratory, has the ability to reflect such dynamic aspects of learning as step-by-step forgetting and strengthening the pieces of knowledge. We explain the basic features of FAVORIT and briefly report on the experiments demonstrating the ability of our approach (1) to cope with noisy and incomplete data and (2) to simplify the internal representation of learned concepts.

Original language	English (US)
Pages (from-to)	19-25
Number of pages	7
Journal	Pattern Recognition Letters
Volume	13
Issue number	1
DOIs	https://doi.org/10.1016/0167-8655(92)90110-L
State	Published - Jan 1992
Externally published	Yes

Keywords

ageing of knowledge
concept formation
Machine learning
prediction of unknown attribute values

ASJC Scopus subject areas

Computer Vision and Pattern Recognition
Signal Processing
Electrical and Electronic Engineering

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10.1016/0167-8655(92)90110-L

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title = "FAVORIT: Concept formation with ageing of knowledge",

abstract = "Concept formation is a machine-learning based approach to cluster analysis of symbolic data. So far, several techniques have been proposed. The system FAVORIT, developed in our laboratory, has the ability to reflect such dynamic aspects of learning as step-by-step forgetting and strengthening the pieces of knowledge. We explain the basic features of FAVORIT and briefly report on the experiments demonstrating the ability of our approach (1) to cope with noisy and incomplete data and (2) to simplify the internal representation of learned concepts.",

keywords = "ageing of knowledge, concept formation, Machine learning, prediction of unknown attribute values",

author = "Ivana Krizakova and Miroslav Kubat",

note = "Funding Information: * This research was partly supported by the grant from Austrian Ministry of Science and Research, under the number GZ 45.002/10-27/91.",

year = "1992",

month = jan,

doi = "10.1016/0167-8655(92)90110-L",

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journal = "Pattern Recognition Letters",

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TY - JOUR

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T2 - Concept formation with ageing of knowledge

AU - Krizakova, Ivana

AU - Kubat, Miroslav

N1 - Funding Information: * This research was partly supported by the grant from Austrian Ministry of Science and Research, under the number GZ 45.002/10-27/91.

PY - 1992/1

Y1 - 1992/1

N2 - Concept formation is a machine-learning based approach to cluster analysis of symbolic data. So far, several techniques have been proposed. The system FAVORIT, developed in our laboratory, has the ability to reflect such dynamic aspects of learning as step-by-step forgetting and strengthening the pieces of knowledge. We explain the basic features of FAVORIT and briefly report on the experiments demonstrating the ability of our approach (1) to cope with noisy and incomplete data and (2) to simplify the internal representation of learned concepts.

AB - Concept formation is a machine-learning based approach to cluster analysis of symbolic data. So far, several techniques have been proposed. The system FAVORIT, developed in our laboratory, has the ability to reflect such dynamic aspects of learning as step-by-step forgetting and strengthening the pieces of knowledge. We explain the basic features of FAVORIT and briefly report on the experiments demonstrating the ability of our approach (1) to cope with noisy and incomplete data and (2) to simplify the internal representation of learned concepts.

KW - ageing of knowledge

KW - concept formation

KW - Machine learning

KW - prediction of unknown attribute values

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JO - Pattern Recognition Letters

JF - Pattern Recognition Letters

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FAVORIT: Concept formation with ageing of knowledge

Abstract

Keywords

ASJC Scopus subject areas

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