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Registro Completo |
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Biblioteca(s): |
Embrapa Agricultura Digital. |
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Data corrente: |
26/08/1996 |
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Data da última atualização: |
10/01/2008 |
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Autoria: |
FISHER, D.; LANGLEY, P. |
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Título: |
Conceptual clustering and its relation to numerical taxonomy. |
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Ano de publicação: |
1986 |
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Fonte/Imprenta: |
In: WORKSHOP ON ARTIFICIAL INTELLIGENCE AND STATISTICS, 1985, Princeton. Artificial intelligence and statistics. Reading: Addison-Wesley, 1986. |
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Páginas: |
p.77-116. |
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Idioma: |
Inglês |
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Notas: |
Edited by Willian A. Gale. |
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Conteúdo: |
Artificial Intelligence (AI) methods for machine learning can be viewed as forms of exploratory data analysis, even though they differ markedly from the statitical methods generally commoted by the term. The distinction between methods of machine learning and statistical data analysis is primarily due to differences in the way techniques of each type represent data and structure within data. That is, methods of machine learning are strongly biased toward symbolic (as opposed to numeric) data representations. We explore this difference within a limited context, devoting the bulk of our chapter to the explication of conceptual clustering, an extension to the statiscally based methods of numerical taxonomy. In conceptual clustering the formation of object clusters is dependent on the quality of 'higher level' characterizations, termed concepts, of the clusters. The form of concepts used by existing conceptual clustering systems (sets of necessary and sufficient conditions) is described in some detail. This is followed by descriptions of several conceptual clustering techniques, along with sample output. We conclude with a discussion of how alternative concept reprsentations might enance the effectiveness of future conceptual clustering systems. |
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Categoria do assunto: |
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Marc: |
LEADER 01783naa a2200157 a 4500
001 1003301
005 2008-01-10
008 1986 bl uuuu u00u1 u #d
100 1 $aFISHER, D.
245 $aConceptual clustering and its relation to numerical taxonomy.
260 $c1986
300 $ap.77-116.
500 $aEdited by Willian A. Gale.
520 $aArtificial Intelligence (AI) methods for machine learning can be viewed as forms of exploratory data analysis, even though they differ markedly from the statitical methods generally commoted by the term. The distinction between methods of machine learning and statistical data analysis is primarily due to differences in the way techniques of each type represent data and structure within data. That is, methods of machine learning are strongly biased toward symbolic (as opposed to numeric) data representations. We explore this difference within a limited context, devoting the bulk of our chapter to the explication of conceptual clustering, an extension to the statiscally based methods of numerical taxonomy. In conceptual clustering the formation of object clusters is dependent on the quality of 'higher level' characterizations, termed concepts, of the clusters. The form of concepts used by existing conceptual clustering systems (sets of necessary and sufficient conditions) is described in some detail. This is followed by descriptions of several conceptual clustering techniques, along with sample output. We conclude with a discussion of how alternative concept reprsentations might enance the effectiveness of future conceptual clustering systems.
700 1 $aLANGLEY, P.
773 $tIn: WORKSHOP ON ARTIFICIAL INTELLIGENCE AND STATISTICS, 1985, Princeton. Artificial intelligence and statistics. Reading: Addison-Wesley, 1986.
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Registro original: |
Embrapa Agricultura Digital (CNPTIA) |
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| Registros recuperados : 4 | |
| 1. |  | FISHER, D. K. Protein consumption dynamics (PCD) model. In: WORKSHOP SBI-AGRO: SOCIEDADE BRASILEIRA DE INFORMÁTICA APLICADA À AGROPECUÁRIA E AGROINDÚSTRIA, 2., 2000, Campinas. Anais... Campinas: Embrapa Informárica Agropecuária, 2000. p. 7-16.| Biblioteca(s): Embrapa Agricultura Digital. |
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| 3. | | HOFMANN, A.; FISHER, D.; ROUWS, L. F. M.; SCHWAB, S.; HARTMANN, A.; BALDANI, J. I. Relevance of the Gluconacetobacter diazotrophicus PAL5 LUX/LUSR quorum sensing system for its plants growth promotion and plant microbe interaction. In: TALLER LATIONOAMERICANO DE PGPR, 3., 2016. Chile. Libro de Resúmenes. Pucón, CH: Universidad de La Frontera, 2016| Tipo: Resumo em Anais de Congresso |
| Biblioteca(s): Embrapa Agrobiologia. |
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| 4. | | SCHOMBERG, H. H.; WIETHÖLTER, S.; GRIFFIN, T. S.; REEVES, D. W.; CABRERA, M. L.; FISHER, D. S.; ENDALE, D. M.; NOVAK, J. M.; BALKCOM, K. S.; RAPER, R. L.; KITCHEN, N. R.; LOCKE, M. A.; POTTER, K. N.; SCHWARTZ, R. C.; TRUMAN, C. C.; TYLER, D. D. Assessing indices for predicting potential nitrogen mineralization in soils under different management systems. Soil Science Society of America Journal, Madison, v. 73, n. 5, p. 1575-1586, 2009.| Tipo: Artigo em Periódico Indexado | Circulação/Nível: A - 1 |
| Biblioteca(s): Embrapa Trigo. |
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| Registros recuperados : 4 | |
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| Nenhum registro encontrado para a expressão de busca informada. |
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