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  • The sensitivity of common knowledge discovery methods to the presence of outlying measurements in the observed data is discussed as their major drawback. Our work is devoted to robust methods for information extraction from data. First, we discuss neural networks for function approximation and their sensitivity to the presence of noise and outlying measurements in the data. We propose to fit neural networks in a robust way by means of a robust nonlinear regression. Secondly, we consider information extraction from categorical data, which commonly suffers from measurement errors. To improve its robustness properties, we propose a regularized version of the common test statistics, which may find applications e.g. in pattern discovery from categorical data.
  • The sensitivity of common knowledge discovery methods to the presence of outlying measurements in the observed data is discussed as their major drawback. Our work is devoted to robust methods for information extraction from data. First, we discuss neural networks for function approximation and their sensitivity to the presence of noise and outlying measurements in the data. We propose to fit neural networks in a robust way by means of a robust nonlinear regression. Secondly, we consider information extraction from categorical data, which commonly suffers from measurement errors. To improve its robustness properties, we propose a regularized version of the common test statistics, which may find applications e.g. in pattern discovery from categorical data. (en)
Title
  • Robustness Aspects of Knowledge Discovery
  • Robustness Aspects of Knowledge Discovery (en)
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  • Robustness Aspects of Knowledge Discovery
  • Robustness Aspects of Knowledge Discovery (en)
skos:notation
  • RIV/67985807:_____/13:00397462!RIV14-AV0-67985807
http://linked.open...avai/predkladatel
http://linked.open...avai/riv/aktivita
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  • I
http://linked.open...vai/riv/dodaniDat
http://linked.open...aciTvurceVysledku
http://linked.open.../riv/druhVysledku
http://linked.open...iv/duvernostUdaju
http://linked.open...titaPredkladatele
http://linked.open...dnocenehoVysledku
  • 103182
http://linked.open...ai/riv/idVysledku
  • RIV/67985807:_____/13:00397462
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • machine learning; outliers; neural networks; robust estimation (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [16E62CDECF85]
http://linked.open...v/mistoKonaniAkce
  • Ostrava
http://linked.open...i/riv/mistoVydani
  • Ostrava
http://linked.open...i/riv/nazevZdroje
  • Datakon a Znalosti 2013. Part II
http://linked.open...in/vavai/riv/obor
http://linked.open...ichTvurcuVysledku
http://linked.open...cetTvurcuVysledku
http://linked.open...UplatneniVysledku
http://linked.open...iv/tvurceVysledku
  • Kalina, Jan
http://linked.open...vavai/riv/typAkce
http://linked.open.../riv/zahajeniAkce
number of pages
http://purl.org/ne...btex#hasPublisher
  • Vysoká škola báňská - Technická univerzita Ostrava
https://schema.org/isbn
  • 978-80-248-3189-3
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