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Description
  • The problem of mining relevant information from large numbers of unstructured text documents is often handled with various machine learning algorithms trained using both positive and negative examples that were prepared by an expert in a~given specific domain. However, when just positive examples are available, the task requires algorithms adapted to the different situation. A~modified k-nearest neighbors algorithm, trained using only positive examples, can classify by way of ranking unlabeled instances depending on their similarity to training examples. This procedure provides a~significant part of unlabeled positive instances with high precision. The main objective is to find a~method for mining relevant documents from large volumes (hundreds or thousands) of similar medical text files.
  • The problem of mining relevant information from large numbers of unstructured text documents is often handled with various machine learning algorithms trained using both positive and negative examples that were prepared by an expert in a~given specific domain. However, when just positive examples are available, the task requires algorithms adapted to the different situation. A~modified k-nearest neighbors algorithm, trained using only positive examples, can classify by way of ranking unlabeled instances depending on their similarity to training examples. This procedure provides a~significant part of unlabeled positive instances with high precision. The main objective is to find a~method for mining relevant documents from large volumes (hundreds or thousands) of similar medical text files. (en)
Title
  • Mining Relevant Text Documents Using Ranking-Based k-NN Algorithms Trained by Only Positive Examples
  • Mining Relevant Text Documents Using Ranking-Based k-NN Algorithms Trained by Only Positive Examples (en)
skos:prefLabel
  • Mining Relevant Text Documents Using Ranking-Based k-NN Algorithms Trained by Only Positive Examples
  • Mining Relevant Text Documents Using Ranking-Based k-NN Algorithms Trained by Only Positive Examples (en)
skos:notation
  • RIV/00216224:14330/05:00013631!RIV10-MSM-14330___
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • Z(MSM 143300003)
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
  • 530366
http://linked.open...ai/riv/idVysledku
  • RIV/00216224:14330/05:00013631
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • ranking; text categorization; k-NN (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [588F791269EC]
http://linked.open...v/mistoKonaniAkce
  • Stará Lesná
http://linked.open...i/riv/mistoVydani
  • Ostrava
http://linked.open...i/riv/nazevZdroje
  • Znalosti 2005, sborník příspěvků
http://linked.open...in/vavai/riv/obor
http://linked.open...ichTvurcuVysledku
http://linked.open...cetTvurcuVysledku
http://linked.open...UplatneniVysledku
http://linked.open...iv/tvurceVysledku
  • Žižka, Jan
  • Hroza, Jiří
http://linked.open...vavai/riv/typAkce
http://linked.open.../riv/zahajeniAkce
http://linked.open...n/vavai/riv/zamer
number of pages
http://purl.org/ne...btex#hasPublisher
  • Vysoká škola báňská - Technická univerzita Ostrava
https://schema.org/isbn
  • 80-248-0755-6
http://localhost/t...ganizacniJednotka
  • 14330
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