Integrating defeasible argumentation and machine learning techniques : Preliminary report

The field of machine learning (ML) is concerned with the question of how to construct algorithms that automatically improve with experience. In recent years many successful ML applications have been developed, such as datamining programs, information-filtering systems, etc. Although ML algorithms al...

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Detalles Bibliográficos
Autores principales: Gómez, Sergio Alejandro, Chesñevar, Carlos Iván
Formato: Objeto de conferencia
Lenguaje:Español
Publicado: 2003
Materias:
Ciencias Informáticas
ARTIFICIAL INTELLIGENCE
Machine Learning
Defeasible Argumentation
Knowledge-based systems
Text mining
Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/21468
Aporte de:
SEDICI (UNLP) de Universidad Nacional de La Plata
id I19-R120-10915-21468
record_format dspace
institution Universidad Nacional de La Plata
institution_str I-19
repository_str R-120
collection SEDICI (UNLP)
language Español
topic Ciencias Informáticas
ARTIFICIAL INTELLIGENCE
Machine Learning
Defeasible Argumentation
Knowledge-based systems
Text mining
spellingShingle Ciencias Informáticas
ARTIFICIAL INTELLIGENCE
Machine Learning
Defeasible Argumentation
Knowledge-based systems
Text mining
Gómez, Sergio Alejandro
Chesñevar, Carlos Iván
Integrating defeasible argumentation and machine learning techniques : Preliminary report
topic_facet Ciencias Informáticas
ARTIFICIAL INTELLIGENCE
Machine Learning
Defeasible Argumentation
Knowledge-based systems
Text mining
description The field of machine learning (ML) is concerned with the question of how to construct algorithms that automatically improve with experience. In recent years many successful ML applications have been developed, such as datamining programs, information-filtering systems, etc. Although ML algorithms allow the detection and extraction of interesting patterns of data for several kinds of problems, most of these algorithms are based on quantitative reasoning, as they rely on training data in order to infer so-called target functions. In the last years defeasible argumentation has proven to be a sound setting to formalize common-sense qualitative reasoning. This approach can be combined with other inference techniques, such as those provided by machine learning theory. In this paper we outline different alternatives for combining defeasible argumentation and machine learning techniques. We suggest how different aspects of a generic argumentbased framework can be integrated with other ML-based approaches.
format Objeto de conferencia
Objeto de conferencia
author Gómez, Sergio Alejandro
Chesñevar, Carlos Iván
author_facet Gómez, Sergio Alejandro
Chesñevar, Carlos Iván
author_sort Gómez, Sergio Alejandro
title Integrating defeasible argumentation and machine learning techniques : Preliminary report
title_short Integrating defeasible argumentation and machine learning techniques : Preliminary report
title_full Integrating defeasible argumentation and machine learning techniques : Preliminary report
title_fullStr Integrating defeasible argumentation and machine learning techniques : Preliminary report
title_full_unstemmed Integrating defeasible argumentation and machine learning techniques : Preliminary report
title_sort integrating defeasible argumentation and machine learning techniques : preliminary report
publishDate 2003
url http://sedici.unlp.edu.ar/handle/10915/21468
work_keys_str_mv AT gomezsergioalejandro integratingdefeasibleargumentationandmachinelearningtechniquespreliminaryreport
AT chesnevarcarlosivan integratingdefeasibleargumentationandmachinelearningtechniquespreliminaryreport
bdutipo_str Repositorios
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