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Description
  • Competitive learning is well-known method to process data. Various goals may be achieved using competi- tive learning such as classifcation or vector quantiza- tion. In this paper, we present a different insight into the principle of supervised competitive learning. An in- novative approach to the supervised self-organization is suggested. The method is based on different handling of input data labels which encode the classifcation. When the label has appropriate format then it is possible to use it within the competitive process in the same way as any input data element. Such approach is as effective as standard supervised methods and has some positive attributes such as the soft classification ability.
  • Competitive learning is well-known method to process data. Various goals may be achieved using competi- tive learning such as classifcation or vector quantiza- tion. In this paper, we present a different insight into the principle of supervised competitive learning. An in- novative approach to the supervised self-organization is suggested. The method is based on different handling of input data labels which encode the classifcation. When the label has appropriate format then it is possible to use it within the competitive process in the same way as any input data element. Such approach is as effective as standard supervised methods and has some positive attributes such as the soft classification ability. (en)
Title
  • Supervised Competition Using Joined Growing Neural Gas
  • Supervised Competition Using Joined Growing Neural Gas (en)
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  • Supervised Competition Using Joined Growing Neural Gas
  • Supervised Competition Using Joined Growing Neural Gas (en)
skos:notation
  • RIV/61989100:27740/14:86090545!RIV15-MSM-27740___
http://linked.open...avai/riv/aktivita
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  • P(EE2.3.30.0055)
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  • 48573
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  • RIV/61989100:27740/14:86090545
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  • Competitive Network, Unsupervised Learning, Super- vised Learning, Vector Quantization, Growing Neural Gas, Classifcation (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [F3F7C5BA9945]
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  • Podhorányi, Michal
  • Fedorčák, Dušan
http://localhost/t...ganizacniJednotka
  • 27740
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