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Registro Completo |
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Biblioteca(s): |
Embrapa Agricultura Digital. |
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Data corrente: |
10/06/2011 |
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Data da última atualização: |
25/07/2011 |
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Autoria: |
LIU, H.; MOTODA, H. (ed.). |
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Afiliação: |
HUAN LIU; HIROSHI MOTODA. |
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Título: |
Computational methods of feature selection. |
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Ano de publicação: |
2008 |
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Fonte/Imprenta: |
Boca Raton: Chapman & Hall/CRC, 2008. |
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Páginas: |
419 p. il. |
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Série: |
(Chapman & Hall/CRC data mining and knowlwdge discovery). |
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ISBN: |
978-1-58488-878-9 |
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Idioma: |
Inglês |
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Conteúdo: |
Less Is More. Huan Liu, Hiroshi Motoda. Unsupervised Feature Selection. Jennifer G. Dy. Randomized Feature Selection. David J. Stracuzzi. Causal Feature Selection. Isabelle Guyon, Constantin Aliferis, André Elisseeff. Active Learning of Feature Relevance. Emanuele Olivetti, Sriharsha Veeramachaneni, Paolo Avesani. A Study of Feature Extraction Techniques Based on Decision Border Estimate. Claudia Diamantini, Domenico Potena. Ensemble-Based Variable Selection Using Independent Probes. Eugene Tuv, Alexander Borisov, Kari Torkkola. Efficient Incremental-Ranked Feature Selection in Massive Data. Roberto Ruiz, Je_us S. Aguilar-Ruiz, Jo_e C. Riquelme. Non-Myopic Feature Quality Evaluation with (R)ReliefF. Igor Kononenko, Marko Robnik Sikonja. Weighting Method for Feature Selection in K-Means. Joshua Zhexue Huang, Jun Xu, Michael Ng, Yunming Ye. Local Feature Selection for Classification. Carlotta Domeniconi, Dimitrios Gunopulos. Feature Weighting through Local Learning. Yijun Sun. Feature Selection for Text Classification. George Forman. A Bayesian Feature Selection Score Based on Naive Bayes Models. Susana Eyheramendy, David Madigan. Pairwise Constraints-Guided Dimensionality Reduction. Wei Tang, Shi Zhong. Aggressive Feature Selection by Feature Ranking. Masoud Makrehchi, Mohamed S. Kamel. Feature Selection for Genomic Data Analysis. Lei Yu. A Feature Generation Algorithm with Applications to Biological Sequence Classification. Rezarta Islamaj Dogan, Lise Getoor, W. John Wilbur. An Ensemble Method for Identifying Robust Features for Biomarker Discovery. Diana Chan, Susan M. Bridges, Shane C. Burgess. Model Building and Feature Selection with Genomic Data. Hui Zou, Trevor Hastie. MenosLess Is More. Huan Liu, Hiroshi Motoda. Unsupervised Feature Selection. Jennifer G. Dy. Randomized Feature Selection. David J. Stracuzzi. Causal Feature Selection. Isabelle Guyon, Constantin Aliferis, André Elisseeff. Active Learning of Feature Relevance. Emanuele Olivetti, Sriharsha Veeramachaneni, Paolo Avesani. A Study of Feature Extraction Techniques Based on Decision Border Estimate. Claudia Diamantini, Domenico Potena. Ensemble-Based Variable Selection Using Independent Probes. Eugene Tuv, Alexander Borisov, Kari Torkkola. Efficient Incremental-Ranked Feature Selection in Massive Data. Roberto Ruiz, Je_us S. Aguilar-Ruiz, Jo_e C. Riquelme. Non-Myopic Feature Quality Evaluation with (R)ReliefF. Igor Kononenko, Marko Robnik Sikonja. Weighting Method for Feature Selection in K-Means. Joshua Zhexue Huang, Jun Xu, Michael Ng, Yunming Ye. Local Feature Selection for Classification. Carlotta Domeniconi, Dimitrios Gunopulos. Feature Weighting through Local Learning. Yijun Sun. Feature Selection for Text Classification. George Forman. A Bayesian Feature Selection Score Based on Naive Bayes Models. Susana Eyheramendy, David Madigan. Pairwise Constraints-Guided Dimensionality Reduction. Wei Tang, Shi Zhong. Aggressive Feature Selection by Feature Ranking. Masoud Makrehchi, Mohamed S. Kamel. Feature Selection for Genomic Data Analysis. Lei Yu. A Feature Generation Algorithm with Applications to Biological Sequence Classification. Rezarta Islamaj Dogan, Lise Getoor, W. John Wilbur.... Mostrar Tudo |
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Palavras-Chave: |
Data mining; Gerenciamento de base de dados; Métodos computacionais; Mineração de dados; Recuperação da informação; Seleção de padrões. |
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Thesaurus Nal: |
Databases; Information retrieval. |
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Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
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Marc: |
LEADER 02442nam a2200253 a 4500
001 1891126
005 2011-07-25
008 2008 bl uuuu 00u1 u #d
020 $a978-1-58488-878-9
100 1 $aLIU, H.
245 $aComputational methods of feature selection.
260 $aBoca Raton: Chapman & Hall/CRC$c2008
300 $a419 p. il.
490 $a(Chapman & Hall/CRC data mining and knowlwdge discovery).
520 $aLess Is More. Huan Liu, Hiroshi Motoda. Unsupervised Feature Selection. Jennifer G. Dy. Randomized Feature Selection. David J. Stracuzzi. Causal Feature Selection. Isabelle Guyon, Constantin Aliferis, André Elisseeff. Active Learning of Feature Relevance. Emanuele Olivetti, Sriharsha Veeramachaneni, Paolo Avesani. A Study of Feature Extraction Techniques Based on Decision Border Estimate. Claudia Diamantini, Domenico Potena. Ensemble-Based Variable Selection Using Independent Probes. Eugene Tuv, Alexander Borisov, Kari Torkkola. Efficient Incremental-Ranked Feature Selection in Massive Data. Roberto Ruiz, Je_us S. Aguilar-Ruiz, Jo_e C. Riquelme. Non-Myopic Feature Quality Evaluation with (R)ReliefF. Igor Kononenko, Marko Robnik Sikonja. Weighting Method for Feature Selection in K-Means. Joshua Zhexue Huang, Jun Xu, Michael Ng, Yunming Ye. Local Feature Selection for Classification. Carlotta Domeniconi, Dimitrios Gunopulos. Feature Weighting through Local Learning. Yijun Sun. Feature Selection for Text Classification. George Forman. A Bayesian Feature Selection Score Based on Naive Bayes Models. Susana Eyheramendy, David Madigan. Pairwise Constraints-Guided Dimensionality Reduction. Wei Tang, Shi Zhong. Aggressive Feature Selection by Feature Ranking. Masoud Makrehchi, Mohamed S. Kamel. Feature Selection for Genomic Data Analysis. Lei Yu. A Feature Generation Algorithm with Applications to Biological Sequence Classification. Rezarta Islamaj Dogan, Lise Getoor, W. John Wilbur. An Ensemble Method for Identifying Robust Features for Biomarker Discovery. Diana Chan, Susan M. Bridges, Shane C. Burgess. Model Building and Feature Selection with Genomic Data. Hui Zou, Trevor Hastie.
650 $aDatabases
650 $aInformation retrieval
653 $aData mining
653 $aGerenciamento de base de dados
653 $aMétodos computacionais
653 $aMineração de dados
653 $aRecuperação da informação
653 $aSeleção de padrões
700 1 $aMOTODA, H.
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Registro original: |
Embrapa Agricultura Digital (CNPTIA) |
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 | Acesso ao texto completo restrito à biblioteca da Embrapa Instrumentação. Para informações adicionais entre em contato com cnpdia.biblioteca@embrapa.br. |
Registro Completo
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Biblioteca(s): |
Embrapa Instrumentação. |
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Data corrente: |
01/03/2005 |
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Data da última atualização: |
25/04/2008 |
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Autoria: |
MELLO, S. V.; MATTOSO, L. H. C.; FARIA, R. M.; OLIVEIRA JÚNIOR, O. N. |
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Título: |
Effect of doping on the fabrication of Langmuir and Langmuir-Blodgett films of poly(o-ethoxyaniline). |
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Ano de publicação: |
1995 |
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Fonte/Imprenta: |
Synthetic Metals, Lausanne, v. 71, p. 2039-2040, 1995. |
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Idioma: |
Inglês |
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Conteúdo: |
The properties of Langmuir films are generally dependent on the pH of the subphase. In a previous work, for instance, we observed that Langmuir films from poly(o-ethoxyaniline) (POEA) suitable for Langmuir-Blodgett deposition were only obtained if spread on an acidic subphase. Because POEA is highly soluble in both undoped and doped states, we have now extended these studies to systematically investigate the effect of doping on the fabrication of Langmuir and LB films. The pressure-area isotherms of the Langmuir films display less hysteresis and higher collapse pressures, when the polymer is doped in solution prior to spreading of the monolayer on an acidic subphase. This procedure leads to stable films that can be readily deposited in the form of LB films. |
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Palavras-Chave: |
Doping; Filmes; POEA. |
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Categoria do assunto: |
-- |
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Marc: |
LEADER 01315naa a2200193 a 4500
001 1028831
005 2008-04-25
008 1995 bl --- 0-- u #d
100 1 $aMELLO, S. V.
245 $aEffect of doping on the fabrication of Langmuir and Langmuir-Blodgett films of poly(o-ethoxyaniline).
260 $c1995
520 $aThe properties of Langmuir films are generally dependent on the pH of the subphase. In a previous work, for instance, we observed that Langmuir films from poly(o-ethoxyaniline) (POEA) suitable for Langmuir-Blodgett deposition were only obtained if spread on an acidic subphase. Because POEA is highly soluble in both undoped and doped states, we have now extended these studies to systematically investigate the effect of doping on the fabrication of Langmuir and LB films. The pressure-area isotherms of the Langmuir films display less hysteresis and higher collapse pressures, when the polymer is doped in solution prior to spreading of the monolayer on an acidic subphase. This procedure leads to stable films that can be readily deposited in the form of LB films.
653 $aDoping
653 $aFilmes
653 $aPOEA
700 1 $aMATTOSO, L. H. C.
700 1 $aFARIA, R. M.
700 1 $aOLIVEIRA JÚNIOR, O. N.
773 $tSynthetic Metals, Lausanne$gv. 71, p. 2039-2040, 1995.
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