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Statements

Subject Item
n2:RIV%2F00216208%3A11320%2F12%3A10129785%21RIV13-GA0-11320___
rdf:type
n8:Vysledek skos:Concept
rdfs:seeAlso
http://link.springer.com/chapter/10.1007%2F978-3-642-33185-5_21
dcterms:description
Inductive Logic Programming (ILP) deals with the problem of finding a hypothesis covering positive examples and excluding negative examples. It uses first-order logic as a uniform representation for examples and hypothesis. In this paper we propose a method to boost any ILP learning algorithm by first decomposing the set of examples to subsets and applying the learning algorithm to each subset separately, second, merging the hypotheses obtained for the subsets to get a single hypothesis for the complete set of examples, and finally refining this single hypothesis to make it shorter. The proposed technique significantly outperforms existing approaches. Inductive Logic Programming (ILP) deals with the problem of finding a hypothesis covering positive examples and excluding negative examples. It uses first-order logic as a uniform representation for examples and hypothesis. In this paper we propose a method to boost any ILP learning algorithm by first decomposing the set of examples to subsets and applying the learning algorithm to each subset separately, second, merging the hypotheses obtained for the subsets to get a single hypothesis for the complete set of examples, and finally refining this single hypothesis to make it shorter. The proposed technique significantly outperforms existing approaches.
dcterms:title
Decomposition, Merging, and Refinement Approach to Boost Inductive Logic Programming Algorithms Decomposition, Merging, and Refinement Approach to Boost Inductive Logic Programming Algorithms
skos:prefLabel
Decomposition, Merging, and Refinement Approach to Boost Inductive Logic Programming Algorithms Decomposition, Merging, and Refinement Approach to Boost Inductive Logic Programming Algorithms
skos:notation
RIV/00216208:11320/12:10129785!RIV13-GA0-11320___
n8:predkladatel
n9:orjk%3A11320
n4:aktivita
n19:P n19:I
n4:aktivity
I, P(GBP202/12/G061)
n4:cisloPeriodika
7557
n4:dodaniDat
n5:2013
n4:domaciTvurceVysledku
n16:8318573 n16:7745346
n4:druhVysledku
n14:J
n4:duvernostUdaju
n21:S
n4:entitaPredkladatele
n13:predkladatel
n4:idSjednocenehoVysledku
129783
n4:idVysledku
RIV/00216208:11320/12:10129785
n4:jazykVysledku
n15:eng
n4:klicovaSlova
Boost Algorithm; Inductive Logic Programming
n4:klicoveSlovo
n11:Boost%20Algorithm n11:Inductive%20Logic%20Programming
n4:kodStatuVydavatele
DE - Spolková republika Německo
n4:kontrolniKodProRIV
[7C73EC12F3AA]
n4:nazevZdroje
Lecture Notes in Computer Science
n4:obor
n17:IN
n4:pocetDomacichTvurcuVysledku
2
n4:pocetTvurcuVysledku
2
n4:projekt
n20:GBP202%2F12%2FG061
n4:rokUplatneniVysledku
n5:2012
n4:svazekPeriodika
2012
n4:tvurceVysledku
Barták, Roman Chovanec, Andrej
s:issn
0302-9743
s:numberOfPages
11
n18:doi
10.1007/978-3-642-33185-5_21
n12:organizacniJednotka
11320