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  • The current situation in environmental monitoring is characterized by increasing amount of data from monitoring networks together with increasing requirements on joining of these data from various sources in comprehensive databases and their usage for decision support in environmental protection and management. The automated analysis of such a heterogeneous datasets is a complicated process, rich in statistical pitfalls. There is a number of methods for multivariate classification of objects, e.g. logistic regression, discriminant analysis or neural networks; however, most of commonly used classification techniques have prerequisites about distribution of data, are computationally demanding or their model can be considered as “black box”. Keeping these facts in mind, we attempted to develop a robust multivariate method suitable for classification of unknown cases with minimum sensitivity to data distribution problems; and thus, suitable for routine use in practice.
  • The current situation in environmental monitoring is characterized by increasing amount of data from monitoring networks together with increasing requirements on joining of these data from various sources in comprehensive databases and their usage for decision support in environmental protection and management. The automated analysis of such a heterogeneous datasets is a complicated process, rich in statistical pitfalls. There is a number of methods for multivariate classification of objects, e.g. logistic regression, discriminant analysis or neural networks; however, most of commonly used classification techniques have prerequisites about distribution of data, are computationally demanding or their model can be considered as “black box”. Keeping these facts in mind, we attempted to develop a robust multivariate method suitable for classification of unknown cases with minimum sensitivity to data distribution problems; and thus, suitable for routine use in practice. (en)
Title
  • Is On-Line Data Analysis Safety? Pitfalls Steaming from Automated Processing of Heterogeneous Environmental Data and Possible Solutions
  • Is On-Line Data Analysis Safety? Pitfalls Steaming from Automated Processing of Heterogeneous Environmental Data and Possible Solutions (en)
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  • Is On-Line Data Analysis Safety? Pitfalls Steaming from Automated Processing of Heterogeneous Environmental Data and Possible Solutions
  • Is On-Line Data Analysis Safety? Pitfalls Steaming from Automated Processing of Heterogeneous Environmental Data and Possible Solutions (en)
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  • RIV/00216224:14110/11:00052824!RIV12-MSM-14110___
http://linked.open...avai/predkladatel
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  • I
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  • 205867
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  • RIV/00216224:14110/11:00052824
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  • classification nonparametric multivariate analysis heterogeneous data (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [D678200BF196]
http://linked.open...v/mistoKonaniAkce
  • Brno
http://linked.open...i/riv/mistoVydani
  • Neuveden
http://linked.open...i/riv/nazevZdroje
  • Environmental Software Systems. Frameworks of eEnvironment. 9th IFIP WG 5.11 International Symposium, ISESS 2011, Brno, Czech Republic, June 27-29, 2011. Proceedings
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http://linked.open...UplatneniVysledku
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  • Dušek, Ladislav
  • Jarkovský, Jiří
  • Janoušová, Eva
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http://linked.open.../riv/zahajeniAkce
number of pages
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  • Springer-Verlag
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  • 978-3-642-22284-9
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  • 14110
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