Concept Formation During Interactive Theory Revision
Abstract This article examines the problem of concept formation in machine learning, and focuses in particular on the problem of aggregation, i .e., the decision of which objects are to be grouped together into a new concept. While existing concept formation approaches have mainly concentrated on ag...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Wrobel, Stefan [verfasserIn] |
|---|
| Format: |
Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
1994 |
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| Anmerkung: |
© Kluwer Academic Publishers 1994 |
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| Übergeordnetes Werk: |
Enthalten in: Machine learning - Kluwer Academic Publishers-Plenum Publishers, 1986, 14(1994), 2 vom: Feb., Seite 169-191 |
|---|---|
| Übergeordnetes Werk: |
volume:14 ; year:1994 ; number:2 ; month:02 ; pages:169-191 |
| Links: |
|---|
| DOI / URN: |
10.1023/A:1022674116380 |
|---|
| Katalog-ID: |
OLC2026513651 |
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10.1023/A:1022674116380 doi (DE-627)OLC2026513651 (DE-He213)A:1022674116380-p DE-627 ger DE-627 rakwb eng 150 004 VZ Wrobel, Stefan verfasserin aut Concept Formation During Interactive Theory Revision 1994 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Kluwer Academic Publishers 1994 Abstract This article examines the problem of concept formation in machine learning, and focuses in particular on the problem of aggregation, i .e., the decision of which objects are to be grouped together into a new concept. While existing concept formation approaches have mainly concentrated on aggregation constraints that rely on structural or correlational properties of the concepts themselves, we argue that in an integrated learning system, other learning activities can provide an additional context that focuses concept formation before structural criteria are applied. In particular, we present the concept formation method realized by the KRT and CLT components of the integrated learning system MOBAL. In MOBAL, a concept formation attempt is triggered whenever no existing concept can adequately capture the rule instance and exception sets as they arise from the theory revision activities of the system. We describe how the so-proposed aggregate is characterized by a set of (function-free) first-order Horn clauses and how these are evaluated according to structural criteria to decide about the introduction of the concept into the representation. We show how a structural criterion can be used to ensure that any new concept improves the structure of the knowledge base, and we empirically evaluate how the introduction of new concepts according to different criteria affects the classification accuracy of learned rules. Enthalten in Machine learning Kluwer Academic Publishers-Plenum Publishers, 1986 14(1994), 2 vom: Feb., Seite 169-191 (DE-627)12920403X (DE-600)54638-0 (DE-576)014457377 0885-6125 nnns volume:14 year:1994 number:2 month:02 pages:169-191 https://doi.org/10.1023/A:1022674116380 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_21 GBV_ILN_22 GBV_ILN_24 GBV_ILN_31 GBV_ILN_70 GBV_ILN_130 GBV_ILN_2006 GBV_ILN_2010 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2093 GBV_ILN_2244 GBV_ILN_4012 GBV_ILN_4046 GBV_ILN_4266 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4318 AR 14 1994 2 02 169-191 |
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10.1023/A:1022674116380 doi (DE-627)OLC2026513651 (DE-He213)A:1022674116380-p DE-627 ger DE-627 rakwb eng 150 004 VZ Wrobel, Stefan verfasserin aut Concept Formation During Interactive Theory Revision 1994 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Kluwer Academic Publishers 1994 Abstract This article examines the problem of concept formation in machine learning, and focuses in particular on the problem of aggregation, i .e., the decision of which objects are to be grouped together into a new concept. While existing concept formation approaches have mainly concentrated on aggregation constraints that rely on structural or correlational properties of the concepts themselves, we argue that in an integrated learning system, other learning activities can provide an additional context that focuses concept formation before structural criteria are applied. In particular, we present the concept formation method realized by the KRT and CLT components of the integrated learning system MOBAL. In MOBAL, a concept formation attempt is triggered whenever no existing concept can adequately capture the rule instance and exception sets as they arise from the theory revision activities of the system. We describe how the so-proposed aggregate is characterized by a set of (function-free) first-order Horn clauses and how these are evaluated according to structural criteria to decide about the introduction of the concept into the representation. We show how a structural criterion can be used to ensure that any new concept improves the structure of the knowledge base, and we empirically evaluate how the introduction of new concepts according to different criteria affects the classification accuracy of learned rules. Enthalten in Machine learning Kluwer Academic Publishers-Plenum Publishers, 1986 14(1994), 2 vom: Feb., Seite 169-191 (DE-627)12920403X (DE-600)54638-0 (DE-576)014457377 0885-6125 nnns volume:14 year:1994 number:2 month:02 pages:169-191 https://doi.org/10.1023/A:1022674116380 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_21 GBV_ILN_22 GBV_ILN_24 GBV_ILN_31 GBV_ILN_70 GBV_ILN_130 GBV_ILN_2006 GBV_ILN_2010 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2093 GBV_ILN_2244 GBV_ILN_4012 GBV_ILN_4046 GBV_ILN_4266 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4318 AR 14 1994 2 02 169-191 |
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10.1023/A:1022674116380 doi (DE-627)OLC2026513651 (DE-He213)A:1022674116380-p DE-627 ger DE-627 rakwb eng 150 004 VZ Wrobel, Stefan verfasserin aut Concept Formation During Interactive Theory Revision 1994 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Kluwer Academic Publishers 1994 Abstract This article examines the problem of concept formation in machine learning, and focuses in particular on the problem of aggregation, i .e., the decision of which objects are to be grouped together into a new concept. While existing concept formation approaches have mainly concentrated on aggregation constraints that rely on structural or correlational properties of the concepts themselves, we argue that in an integrated learning system, other learning activities can provide an additional context that focuses concept formation before structural criteria are applied. In particular, we present the concept formation method realized by the KRT and CLT components of the integrated learning system MOBAL. In MOBAL, a concept formation attempt is triggered whenever no existing concept can adequately capture the rule instance and exception sets as they arise from the theory revision activities of the system. We describe how the so-proposed aggregate is characterized by a set of (function-free) first-order Horn clauses and how these are evaluated according to structural criteria to decide about the introduction of the concept into the representation. We show how a structural criterion can be used to ensure that any new concept improves the structure of the knowledge base, and we empirically evaluate how the introduction of new concepts according to different criteria affects the classification accuracy of learned rules. Enthalten in Machine learning Kluwer Academic Publishers-Plenum Publishers, 1986 14(1994), 2 vom: Feb., Seite 169-191 (DE-627)12920403X (DE-600)54638-0 (DE-576)014457377 0885-6125 nnns volume:14 year:1994 number:2 month:02 pages:169-191 https://doi.org/10.1023/A:1022674116380 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_21 GBV_ILN_22 GBV_ILN_24 GBV_ILN_31 GBV_ILN_70 GBV_ILN_130 GBV_ILN_2006 GBV_ILN_2010 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2093 GBV_ILN_2244 GBV_ILN_4012 GBV_ILN_4046 GBV_ILN_4266 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4318 AR 14 1994 2 02 169-191 |
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10.1023/A:1022674116380 doi (DE-627)OLC2026513651 (DE-He213)A:1022674116380-p DE-627 ger DE-627 rakwb eng 150 004 VZ Wrobel, Stefan verfasserin aut Concept Formation During Interactive Theory Revision 1994 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Kluwer Academic Publishers 1994 Abstract This article examines the problem of concept formation in machine learning, and focuses in particular on the problem of aggregation, i .e., the decision of which objects are to be grouped together into a new concept. While existing concept formation approaches have mainly concentrated on aggregation constraints that rely on structural or correlational properties of the concepts themselves, we argue that in an integrated learning system, other learning activities can provide an additional context that focuses concept formation before structural criteria are applied. In particular, we present the concept formation method realized by the KRT and CLT components of the integrated learning system MOBAL. In MOBAL, a concept formation attempt is triggered whenever no existing concept can adequately capture the rule instance and exception sets as they arise from the theory revision activities of the system. We describe how the so-proposed aggregate is characterized by a set of (function-free) first-order Horn clauses and how these are evaluated according to structural criteria to decide about the introduction of the concept into the representation. We show how a structural criterion can be used to ensure that any new concept improves the structure of the knowledge base, and we empirically evaluate how the introduction of new concepts according to different criteria affects the classification accuracy of learned rules. Enthalten in Machine learning Kluwer Academic Publishers-Plenum Publishers, 1986 14(1994), 2 vom: Feb., Seite 169-191 (DE-627)12920403X (DE-600)54638-0 (DE-576)014457377 0885-6125 nnns volume:14 year:1994 number:2 month:02 pages:169-191 https://doi.org/10.1023/A:1022674116380 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_21 GBV_ILN_22 GBV_ILN_24 GBV_ILN_31 GBV_ILN_70 GBV_ILN_130 GBV_ILN_2006 GBV_ILN_2010 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2093 GBV_ILN_2244 GBV_ILN_4012 GBV_ILN_4046 GBV_ILN_4266 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4318 AR 14 1994 2 02 169-191 |
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10.1023/A:1022674116380 doi (DE-627)OLC2026513651 (DE-He213)A:1022674116380-p DE-627 ger DE-627 rakwb eng 150 004 VZ Wrobel, Stefan verfasserin aut Concept Formation During Interactive Theory Revision 1994 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Kluwer Academic Publishers 1994 Abstract This article examines the problem of concept formation in machine learning, and focuses in particular on the problem of aggregation, i .e., the decision of which objects are to be grouped together into a new concept. While existing concept formation approaches have mainly concentrated on aggregation constraints that rely on structural or correlational properties of the concepts themselves, we argue that in an integrated learning system, other learning activities can provide an additional context that focuses concept formation before structural criteria are applied. In particular, we present the concept formation method realized by the KRT and CLT components of the integrated learning system MOBAL. In MOBAL, a concept formation attempt is triggered whenever no existing concept can adequately capture the rule instance and exception sets as they arise from the theory revision activities of the system. We describe how the so-proposed aggregate is characterized by a set of (function-free) first-order Horn clauses and how these are evaluated according to structural criteria to decide about the introduction of the concept into the representation. We show how a structural criterion can be used to ensure that any new concept improves the structure of the knowledge base, and we empirically evaluate how the introduction of new concepts according to different criteria affects the classification accuracy of learned rules. Enthalten in Machine learning Kluwer Academic Publishers-Plenum Publishers, 1986 14(1994), 2 vom: Feb., Seite 169-191 (DE-627)12920403X (DE-600)54638-0 (DE-576)014457377 0885-6125 nnns volume:14 year:1994 number:2 month:02 pages:169-191 https://doi.org/10.1023/A:1022674116380 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_21 GBV_ILN_22 GBV_ILN_24 GBV_ILN_31 GBV_ILN_70 GBV_ILN_130 GBV_ILN_2006 GBV_ILN_2010 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2093 GBV_ILN_2244 GBV_ILN_4012 GBV_ILN_4046 GBV_ILN_4266 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4318 AR 14 1994 2 02 169-191 |
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Concept Formation During Interactive Theory Revision |
| abstract |
Abstract This article examines the problem of concept formation in machine learning, and focuses in particular on the problem of aggregation, i .e., the decision of which objects are to be grouped together into a new concept. While existing concept formation approaches have mainly concentrated on aggregation constraints that rely on structural or correlational properties of the concepts themselves, we argue that in an integrated learning system, other learning activities can provide an additional context that focuses concept formation before structural criteria are applied. In particular, we present the concept formation method realized by the KRT and CLT components of the integrated learning system MOBAL. In MOBAL, a concept formation attempt is triggered whenever no existing concept can adequately capture the rule instance and exception sets as they arise from the theory revision activities of the system. We describe how the so-proposed aggregate is characterized by a set of (function-free) first-order Horn clauses and how these are evaluated according to structural criteria to decide about the introduction of the concept into the representation. We show how a structural criterion can be used to ensure that any new concept improves the structure of the knowledge base, and we empirically evaluate how the introduction of new concepts according to different criteria affects the classification accuracy of learned rules. © Kluwer Academic Publishers 1994 |
| abstractGer |
Abstract This article examines the problem of concept formation in machine learning, and focuses in particular on the problem of aggregation, i .e., the decision of which objects are to be grouped together into a new concept. While existing concept formation approaches have mainly concentrated on aggregation constraints that rely on structural or correlational properties of the concepts themselves, we argue that in an integrated learning system, other learning activities can provide an additional context that focuses concept formation before structural criteria are applied. In particular, we present the concept formation method realized by the KRT and CLT components of the integrated learning system MOBAL. In MOBAL, a concept formation attempt is triggered whenever no existing concept can adequately capture the rule instance and exception sets as they arise from the theory revision activities of the system. We describe how the so-proposed aggregate is characterized by a set of (function-free) first-order Horn clauses and how these are evaluated according to structural criteria to decide about the introduction of the concept into the representation. We show how a structural criterion can be used to ensure that any new concept improves the structure of the knowledge base, and we empirically evaluate how the introduction of new concepts according to different criteria affects the classification accuracy of learned rules. © Kluwer Academic Publishers 1994 |
| abstract_unstemmed |
Abstract This article examines the problem of concept formation in machine learning, and focuses in particular on the problem of aggregation, i .e., the decision of which objects are to be grouped together into a new concept. While existing concept formation approaches have mainly concentrated on aggregation constraints that rely on structural or correlational properties of the concepts themselves, we argue that in an integrated learning system, other learning activities can provide an additional context that focuses concept formation before structural criteria are applied. In particular, we present the concept formation method realized by the KRT and CLT components of the integrated learning system MOBAL. In MOBAL, a concept formation attempt is triggered whenever no existing concept can adequately capture the rule instance and exception sets as they arise from the theory revision activities of the system. We describe how the so-proposed aggregate is characterized by a set of (function-free) first-order Horn clauses and how these are evaluated according to structural criteria to decide about the introduction of the concept into the representation. We show how a structural criterion can be used to ensure that any new concept improves the structure of the knowledge base, and we empirically evaluate how the introduction of new concepts according to different criteria affects the classification accuracy of learned rules. © Kluwer Academic Publishers 1994 |
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| title_short |
Concept Formation During Interactive Theory Revision |
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