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Statements

Subject Item
n2:RIV%2F68407700%3A21230%2F10%3A00171012%21RIV11-MSM-21230___
rdf:type
n3:Vysledek skos:Concept
dcterms:description
In this paper we describe a new approach to the application of evolutionary stochastic search in Inductive Logic Programming (ILP). Unlike traditional approaches that focus on evolving populations of logical clauses, our refinement-based approach uses the stochastic optimization process to iteratively adapt initial working clause. Utilization of context-sensitive concept refinements (adaptations) helps the search operations to produce mostly syntactically correct concepts and enables using available background knowledge both for efficiently restricting the search space and for directing the search. Thereby, the search is more flexible, less problem-specific and the framework can be easily used with any stochastic search algorithm within ILP domain. Experimental results on several data sets verify the usefulness of this approach. In this paper we describe a new approach to the application of evolutionary stochastic search in Inductive Logic Programming (ILP). Unlike traditional approaches that focus on evolving populations of logical clauses, our refinement-based approach uses the stochastic optimization process to iteratively adapt initial working clause. Utilization of context-sensitive concept refinements (adaptations) helps the search operations to produce mostly syntactically correct concepts and enables using available background knowledge both for efficiently restricting the search space and for directing the search. Thereby, the search is more flexible, less problem-specific and the framework can be easily used with any stochastic search algorithm within ILP domain. Experimental results on several data sets verify the usefulness of this approach.
dcterms:title
Context-sensitive Refinements for Stochastic Optimization Algorithms in Inductive Logic Programming Context-sensitive Refinements for Stochastic Optimization Algorithms in Inductive Logic Programming
skos:prefLabel
Context-sensitive Refinements for Stochastic Optimization Algorithms in Inductive Logic Programming Context-sensitive Refinements for Stochastic Optimization Algorithms in Inductive Logic Programming
skos:notation
RIV/68407700:21230/10:00171012!RIV11-MSM-21230___
n4:aktivita
n8:Z
n4:aktivity
Z(MSM6840770012)
n4:dodaniDat
n5:2011
n4:domaciTvurceVysledku
n12:1775022 n12:5775590
n4:druhVysledku
n16:D
n4:duvernostUdaju
n18:S
n4:entitaPredkladatele
n10:predkladatel
n4:idSjednocenehoVysledku
251971
n4:idVysledku
RIV/68407700:21230/10:00171012
n4:jazykVysledku
n15:eng
n4:klicovaSlova
optimization; evolutionary algorithms; inductive logic programming
n4:klicoveSlovo
n7:evolutionary%20algorithms n7:inductive%20logic%20programming n7:optimization
n4:kontrolniKodProRIV
[1E86DA759E02]
n4:mistoKonaniAkce
Portland, Oregon
n4:mistoVydani
New York
n4:nazevZdroje
Proceedings of the 12th annual conference comp on Genetic and evolutionary computation
n4:obor
n19:JC
n4:pocetDomacichTvurcuVysledku
2
n4:pocetTvurcuVysledku
2
n4:rokUplatneniVysledku
n5:2010
n4:tvurceVysledku
Kubalík, Jiří Buryan, Petr
n4:typAkce
n6:WRD
n4:zahajeniAkce
2010-07-07+02:00
n4:zamer
n17:MSM6840770012
s:numberOfPages
2
n9:hasPublisher
ACM
n20:isbn
978-1-4503-0073-5
n14:organizacniJednotka
21230