About: Decomposition, Merging, and Refinement Approach to Boost Inductive Logic Programming Algorithms

Facets (new session)
Description
Metadata
Settings
- owl:sameAs
- Inference Rule:

About: Decomposition, Merging, and Refinement Approach to Boost Inductive Logic Programming 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)

Attributes	Values
rdf:type	skos:Concept http://linked.opendata.cz/ontology/domain/vavai/Vysledek
rdfs:seeAlso	http://link.springer.com/chapter/10.1007%2F978-3-642-33185-5_21
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. (en)
Title	Decomposition, Merging, and Refinement Approach to Boost Inductive Logic Programming Algorithms Decomposition, Merging, and Refinement Approach to Boost Inductive Logic Programming Algorithms (en)
skos:prefLabel	Decomposition, Merging, and Refinement Approach to Boost Inductive Logic Programming Algorithms Decomposition, Merging, and Refinement Approach to Boost Inductive Logic Programming Algorithms (en)
skos:notation	RIV/00216208:11320/12:10129785!RIV13-GA0-11320___
http://linked.open...avai/riv/aktivita	I P
http://linked.open...avai/riv/aktivity	I, P(GBP202/12/G061)
http://linked.open...iv/cisloPeriodika	7557
http://linked.open...vai/riv/dodaniDat	2013
http://linked.open...aciTvurceVysledku	Chovanec, Andrej Barták, Roman
http://linked.open.../riv/druhVysledku	J - Článek v odborném periodiku
http://linked.open...iv/duvernostUdaju	S - Úplné a pravdivé údaje nepodléhající ochraně podle zvláštních právních předpisů
http://linked.open...titaPredkladatele	Univerzita Karlova v Praze / Matematicko-fyzikální fakulta
http://linked.open...dnocenehoVysledku	129783
http://linked.open...ai/riv/idVysledku	RIV/00216208:11320/12:10129785
http://linked.open...riv/jazykVysledku	eng - angličtina
http://linked.open.../riv/klicovaSlova	Boost Algorithm; Inductive Logic Programming (en)
http://linked.open.../riv/klicoveSlovo	Inductive Logic Programming Boost Algorithm
http://linked.open...odStatuVydavatele	DE - Spolková republika Německo
http://linked.open...ontrolniKodProRIV	[7C73EC12F3AA]
http://linked.open...i/riv/nazevZdroje	Lecture Notes in Computer Science
http://linked.open...in/vavai/riv/obor	IN
http://linked.open...ichTvurcuVysledku	2 (xsd:int)
http://linked.open...cetTvurcuVysledku	2 (xsd:int)
http://linked.open...vavai/riv/projekt	Center of excellence - Institute for theoretical computer science (CE-ITI)
http://linked.open...UplatneniVysledku	2012
http://linked.open...v/svazekPeriodika	2012
http://linked.open...iv/tvurceVysledku	Barták, Roman Chovanec, Andrej
issn	0302-9743
number of pages	11 (xsd:int)
http://bibframe.org/vocab/doi	10.1007/978-3-642-33185-5_21
http://localhost/t...ganizacniJednotka	11320

Faceted Search & Find service v1.16.118 as of Jun 21 2024

Alternative Linked Data Documents: ODE Content Formats:

RDF

ODATA

Microdata

About

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, 47 GB memory in use)
Data on this page belongs to its respective rights holders.
Virtuoso Faceted Browser Copyright © 2009-2024 OpenLink Software