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  • This research deals with a novel approach to classification. This paper deals with a synthesis of a complex structure, which serves as a classifier. This structure is similar to classical artificial neural net therefore the name pseudo neural network is used. The proposed method for classifier structure synthesis utilizes Analytic Programming (AP) as the tool of the evolutionary symbolic regression. AP synthesizes a whole structure of the relation between inputs and output. Classical artificial neural networks, where a relation between inputs and outputs is based on the mathematical transfer functions and optimized numerical weights, were an inspiration for this work. The paper shows two approaches – continues classification with one output node and classical approach with binary classification and more output nodes. Lenses data (one of benchmarks for classifiers) was used for testing of the proposed method. For experimentation, Differential Evolution for the main procedure and also for meta-evolution version of analytic programming was used.
  • This research deals with a novel approach to classification. This paper deals with a synthesis of a complex structure, which serves as a classifier. This structure is similar to classical artificial neural net therefore the name pseudo neural network is used. The proposed method for classifier structure synthesis utilizes Analytic Programming (AP) as the tool of the evolutionary symbolic regression. AP synthesizes a whole structure of the relation between inputs and output. Classical artificial neural networks, where a relation between inputs and outputs is based on the mathematical transfer functions and optimized numerical weights, were an inspiration for this work. The paper shows two approaches – continues classification with one output node and classical approach with binary classification and more output nodes. Lenses data (one of benchmarks for classifiers) was used for testing of the proposed method. For experimentation, Differential Evolution for the main procedure and also for meta-evolution version of analytic programming was used. (en)
Title
  • Lenses Classification by means of Pseudo Neural Networks – Two Approaches
  • Lenses Classification by means of Pseudo Neural Networks – Two Approaches (en)
skos:prefLabel
  • Lenses Classification by means of Pseudo Neural Networks – Two Approaches
  • Lenses Classification by means of Pseudo Neural Networks – Two Approaches (en)
skos:notation
  • RIV/70883521:28140/14:43871644!RIV15-MSM-28140___
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • P(ED2.1.00/03.0089)
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
  • 25978
http://linked.open...ai/riv/idVysledku
  • RIV/70883521:28140/14:43871644
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • classification.; symbolic regression; Pseudo neural networks (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [AFD8E51485E9]
http://linked.open...v/mistoKonaniAkce
  • Brno
http://linked.open...i/riv/mistoVydani
  • Brno
http://linked.open...i/riv/nazevZdroje
  • MENDEL 2014 20 th International conference on soft Computing
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
  • Šenkeřík, Roman
  • Komínková Oplatková, Zuzana
http://linked.open...vavai/riv/typAkce
http://linked.open.../riv/zahajeniAkce
issn
  • 1803-3814
number of pages
http://purl.org/ne...btex#hasPublisher
  • Vysoké učení technické v Brně. Fakulta strojního inženýrství
https://schema.org/isbn
  • 978-80-214-4984-8
http://localhost/t...ganizacniJednotka
  • 28140
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