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Description
  • In machine learning, one of the main requirements is to build computational models with a high ability to generalize well the extracted knowledge. When training e.g. artificial neural networks, poor generalization is often characterized by over-training. A common method to avoid over-training is the hold-out cross-validation. The basic problem of this method represents, however, appropriate data splitting. In most of the applications, simple random sampling is used. Nevertheless, there are several sophisticated statistical sampling methods suitable for various types of datasets. This paper provides a survey of existing sampling methods applicable to the data splitting problem. Supporting experiments evaluating the benefits of the selected data splitting techniques involve artificial neural networks of the back-propagation type.
  • In machine learning, one of the main requirements is to build computational models with a high ability to generalize well the extracted knowledge. When training e.g. artificial neural networks, poor generalization is often characterized by over-training. A common method to avoid over-training is the hold-out cross-validation. The basic problem of this method represents, however, appropriate data splitting. In most of the applications, simple random sampling is used. Nevertheless, there are several sophisticated statistical sampling methods suitable for various types of datasets. This paper provides a survey of existing sampling methods applicable to the data splitting problem. Supporting experiments evaluating the benefits of the selected data splitting techniques involve artificial neural networks of the back-propagation type. (en)
Title
  • Data Splitting
  • Data Splitting (en)
skos:prefLabel
  • Data Splitting
  • Data Splitting (en)
skos:notation
  • RIV/00216208:11320/10:10080310!RIV11-GA0-11320___
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • P(GD201/09/H057), S
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
  • 252949
http://linked.open...ai/riv/idVysledku
  • RIV/00216208:11320/10:10080310
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • Machine Learning; Hold-out cross-validation; Sampling; Data splitting (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [2AE33040BE52]
http://linked.open...v/mistoKonaniAkce
  • Praha
http://linked.open...i/riv/mistoVydani
  • Praha
http://linked.open...i/riv/nazevZdroje
  • WDS'10 Proceedings of Contributed Papers: Part I - Mathematics and Computer Sciences
http://linked.open...in/vavai/riv/obor
http://linked.open...ichTvurcuVysledku
http://linked.open...cetTvurcuVysledku
http://linked.open...vavai/riv/projekt
http://linked.open...UplatneniVysledku
http://linked.open...iv/tvurceVysledku
  • Reitermanová, Zuzana
http://linked.open...vavai/riv/typAkce
http://linked.open.../riv/zahajeniAkce
number of pages
http://purl.org/ne...btex#hasPublisher
  • Matfyzpress
https://schema.org/isbn
  • 978-80-7378-139-2
http://localhost/t...ganizacniJednotka
  • 11320
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