KAVAS-2: Knowledge acquisition, visualization and assessment system

Jan Talmon; Jytte Brender; Michel Demeester; Panayotis Drosos; Jane Grimson; A.-M. Langsig; Peter McNair; Pirkko Nykänen; Rory O'Moore; A. Rossi-Mori; S. Weber; W. Wilhelm

doi:10.1016/0169-2607(94)90026-4

KAVAS-2: Knowledge acquisition, visualization and assessment system

Jan Talmon (Corresponding Author), Jytte Brender, Michel Demeester, Panayotis Drosos, Jane Grimson, A.-M. Langsig, Peter McNair, Pirkko Nykänen, Rory O'Moore, A. Rossi-Mori, S. Weber, W. Wilhelm

VTT Technical Research Centre of Finland

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

Abstract

The objective of KAVAS-2 is the development of a tool, named KAVIAR, with which domain experts can make their knowledge explicit. It contains components for (computer assisted) knowledge elicitation and for machine learning. A key issue in KAVAS is the assessment of the quality of the classification and domain models built. Various quality measures are available and implemented in KAVIAR to assess the quality of models, specifically those developed from data bases by machine learning techniques.

Original language	English
Pages (from-to)	105-109
Number of pages	5
Journal	Computer Methods and Programs in Biomedicine
Volume	45
Issue number	1-2
DOIs	https://doi.org/10.1016/0169-2607(94)90026-4
Publication status	Published - 1994
MoE publication type	A1 Journal article-refereed

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@article{f076ebfc36c24317be26e18136e0b448,

title = "KAVAS-2: Knowledge acquisition, visualization and assessment system",

abstract = "The objective of KAVAS-2 is the development of a tool, named KAVIAR, with which domain experts can make their knowledge explicit. It contains components for (computer assisted) knowledge elicitation and for machine learning. A key issue in KAVAS is the assessment of the quality of the classification and domain models built. Various quality measures are available and implemented in KAVIAR to assess the quality of models, specifically those developed from data bases by machine learning techniques.",

author = "Jan Talmon and Jytte Brender and Michel Demeester and Panayotis Drosos and Jane Grimson and A.-M. Langsig and Peter McNair and Pirkko Nyk{\"a}nen and Rory O'Moore and A. Rossi-Mori and S. Weber and W. Wilhelm",

note = "Project code: TTE2112",

year = "1994",

doi = "10.1016/0169-2607(94)90026-4",

language = "English",

volume = "45",

pages = "105--109",

journal = "Computer Methods and Programs in Biomedicine",

issn = "0169-2607",

publisher = "Elsevier",

number = "1-2",

}

KAVAS-2 : Knowledge acquisition, visualization and assessment system. / Talmon, Jan (Corresponding Author); Brender, Jytte; Demeester, Michel; Drosos, Panayotis; Grimson, Jane; Langsig, A.-M.; McNair, Peter; Nykänen, Pirkko; O'Moore, Rory; Rossi-Mori, A.; Weber, S.; Wilhelm, W.

In: Computer Methods and Programs in Biomedicine, Vol. 45, No. 1-2, 1994, p. 105-109.

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

TY - JOUR

T1 - KAVAS-2

T2 - Knowledge acquisition, visualization and assessment system

AU - Talmon, Jan

AU - Brender, Jytte

AU - Demeester, Michel

AU - Drosos, Panayotis

AU - Grimson, Jane

AU - Langsig, A.-M.

AU - McNair, Peter

AU - Nykänen, Pirkko

AU - O'Moore, Rory

AU - Rossi-Mori, A.

AU - Weber, S.

AU - Wilhelm, W.

N1 - Project code: TTE2112

PY - 1994

Y1 - 1994

N2 - The objective of KAVAS-2 is the development of a tool, named KAVIAR, with which domain experts can make their knowledge explicit. It contains components for (computer assisted) knowledge elicitation and for machine learning. A key issue in KAVAS is the assessment of the quality of the classification and domain models built. Various quality measures are available and implemented in KAVIAR to assess the quality of models, specifically those developed from data bases by machine learning techniques.

AB - The objective of KAVAS-2 is the development of a tool, named KAVIAR, with which domain experts can make their knowledge explicit. It contains components for (computer assisted) knowledge elicitation and for machine learning. A key issue in KAVAS is the assessment of the quality of the classification and domain models built. Various quality measures are available and implemented in KAVIAR to assess the quality of models, specifically those developed from data bases by machine learning techniques.

U2 - 10.1016/0169-2607(94)90026-4

DO - 10.1016/0169-2607(94)90026-4

M3 - Article

VL - 45

SP - 105

EP - 109

JO - Computer Methods and Programs in Biomedicine

JF - Computer Methods and Programs in Biomedicine

SN - 0169-2607

IS - 1-2

ER -