Efficient Sampling in Relational Feature Spaces

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F05%3A03109208" target="_blank" >RIV/68407700:21230/05:03109208 - isvavai.cz</a>

Result on the web

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DOI - Digital Object Identifier

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Result language

angličtina

Original language name

Efficient Sampling in Relational Feature Spaces

Original language description

State-of-the-art algorithms implementing the `extended transformation approach' to propositionalization use backtrack depth first search for the construction of relational features (first order atom conjunctions) complying to user's mode/type declarations and a few basic syntactic conditions. As such they incur a complexity factor exponential in the maximum allowed feature size. Here I present an alternative based on an efficient reduction of the feature construction problem on the propositional satisfiability (SAT) problem, such that the latter involves only Horn clauses and is therefore tractable: a model to a propositional Horn theory can be found without backtracking in time linear in the number of literals contained.

Czech name

Není k dispozici

Czech description

Není k dispozici

Type

D - Article in proceedings

CEP classification

JC - Computer hardware and software

OECD FORD branch

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Project

<a href="/en/project/1ET101210513" target="_blank" >1ET101210513: Relational machine learning for analysis of biomedical data</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2005

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

Inductive Logic Programming

ISBN

3-540-28177-0

ISSN

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e-ISSN

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Number of pages

17

Pages from-to

397-413

Publisher name

Springer

Place of publication

Berlin

Event location

Bonn

Event date

Aug 10, 2005

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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