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Description
  • This paper introduces a novel approach to novelty detection of every individual sample of data in a time series. The novelty detection is based on the knowledge learned by neural networks and the consistency of data with contemporary governing law. In particular, the relationship of prediction error with the adaptive weight increments by gradient decent is shown, as the modification of the recently introduced adaptive approach of novelty detection. Static and dynamic neural network models are shown on theoretical data as well as on a real ECG signal.
  • This paper introduces a novel approach to novelty detection of every individual sample of data in a time series. The novelty detection is based on the knowledge learned by neural networks and the consistency of data with contemporary governing law. In particular, the relationship of prediction error with the adaptive weight increments by gradient decent is shown, as the modification of the recently introduced adaptive approach of novelty detection. Static and dynamic neural network models are shown on theoretical data as well as on a real ECG signal. (en)
Title
  • Another Adaptive Approach to Novelty Detection in Time Series
  • Another Adaptive Approach to Novelty Detection in Time Series (en)
skos:prefLabel
  • Another Adaptive Approach to Novelty Detection in Time Series
  • Another Adaptive Approach to Novelty Detection in Time Series (en)
skos:notation
  • RIV/68407700:21220/14:00212030!RIV15-MSM-21220___
http://linked.open...avai/riv/aktivita
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  • S
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  • 3345
http://linked.open...ai/riv/idVysledku
  • RIV/68407700:21220/14:00212030
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  • Novelty Detection; Time Series; Gradient Descent; Neural Networks; ECG (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [D2DBD2091820]
http://linked.open...v/mistoKonaniAkce
  • Sydney
http://linked.open...i/riv/mistoVydani
  • Chennai, Tamil Nadu
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  • Computer Science & Information Technology
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  • Bukovský, Ivo
  • Beneš, Peter Mark
  • Cejnek, Matouš
http://linked.open...vavai/riv/typAkce
http://linked.open.../riv/zahajeniAkce
issn
  • 2231-5403
number of pages
http://bibframe.org/vocab/doi
  • 10.5121/csit.2014.4229
http://purl.org/ne...btex#hasPublisher
  • AIRCC Publishing Corporation
http://localhost/t...ganizacniJednotka
  • 21220
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