About: Optimizing Models Using Continuous Ant Algorithms     Goto   Sponge   NotDistinct   Permalink

An Entity of Type : http://linked.opendata.cz/ontology/domain/vavai/Vysledek, within Data Space : linked.opendata.cz associated with source document(s)

AttributesValues
rdf:type
Description
  • While constructing inductive models of a given system, we need to optimize parameters of units the system is composed of. These parameters are often real-valued variables and we can use a large scale of continuous optimization methods to locate their optimum. Each of these methods can give different results for problems of various nature or complexity. In our experiments, the usually best performing gradient based Quasi-Newton method was unable to optimize parameters for a well known problem of two intertwined spirals; its classification accuracy was close to 50%. Therefore, we compared several continuous optimization algorithms performance on this particular problem. Our results show that two probabilistic algorithms inspired by ant behaviour are able to optimize parameters of model units for this problem with the classification accuracy of 70%.
  • While constructing inductive models of a given system, we need to optimize parameters of units the system is composed of. These parameters are often real-valued variables and we can use a large scale of continuous optimization methods to locate their optimum. Each of these methods can give different results for problems of various nature or complexity. In our experiments, the usually best performing gradient based Quasi-Newton method was unable to optimize parameters for a well known problem of two intertwined spirals; its classification accuracy was close to 50%. Therefore, we compared several continuous optimization algorithms performance on this particular problem. Our results show that two probabilistic algorithms inspired by ant behaviour are able to optimize parameters of model units for this problem with the classification accuracy of 70%. (en)
  • While constructing inductive models of a given system, we need to optimize parameters of units the system is composed of. These parameters are often real-valued variables and we can use a large scale of continuous optimization methods to locate their optimum. Each of these methods can give different results for problems of various nature or complexity. In our experiments, the usually best performing gradient based Quasi-Newton method was unable to optimize parameters for a well known problem of two intertwined spirals; its classification accuracy was close to 50%. Therefore, we compared several continuous optimization algorithms performance on this particular problem. Our results show that two probabilistic algorithms inspired by ant behaviour are able to optimize parameters of model units for this problem with the classification accuracy of 70%. (cs)
Title
  • Optimizing Models Using Continuous Ant Algorithms
  • Optimizing Models Using Continuous Ant Algorithms (en)
  • Optimizing Models Using Continuous Ant Algorithms (cs)
skos:prefLabel
  • Optimizing Models Using Continuous Ant Algorithms
  • Optimizing Models Using Continuous Ant Algorithms (en)
  • Optimizing Models Using Continuous Ant Algorithms (cs)
skos:notation
  • RIV/68407700:21230/08:03145837!RIV09-MSM-21230___
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • P(KJB201210701), Z(MSM6840770012)
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
  • 385273
http://linked.open...ai/riv/idVysledku
  • RIV/68407700:21230/08:03145837
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • Ant Algorithms; Continuous Optimization; Inductive Modelling (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [B5E70E22104F]
http://linked.open...v/mistoKonaniAkce
  • Kyjev
http://linked.open...i/riv/mistoVydani
  • Kiev
http://linked.open...i/riv/nazevZdroje
  • Proceedings of the 2nd International Conference on Inductive Modelling
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
  • Kordík, Pavel
  • Kovářík, Oleg
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
  • Ukr. INTEI
https://schema.org/isbn
  • 978-966-02-4889-2
http://localhost/t...ganizacniJednotka
  • 21230
is http://linked.open...avai/riv/vysledek of
Faceted Search & Find service v1.16.118 as of Jun 21 2024


Alternative Linked Data Documents: ODE     Content Formats:   [cxml] [csv]     RDF   [text] [turtle] [ld+json] [rdf+json] [rdf+xml]     ODATA   [atom+xml] [odata+json]     Microdata   [microdata+json] [html]    About   
This material is Open Knowledge   W3C Semantic Web Technology [RDF Data] Valid XHTML + RDFa
OpenLink Virtuoso version 07.20.3240 as of Jun 21 2024, on Linux (x86_64-pc-linux-gnu), Single-Server Edition (126 GB total memory, 58 GB memory in use)
Data on this page belongs to its respective rights holders.
Virtuoso Faceted Browser Copyright © 2009-2024 OpenLink Software