Título

Using wittgenstein’s family resemblance principle to learn exemplars

Autor

ANDRES FLORENCIO RODRIGUEZ MARTINEZ

LUIS ENRIQUE SUCAR SUCCAR

Jia Wu

Nivel de Acceso

Acceso Abierto

Licencia

http://creativecommons.org/licenses/by-nc-nd/4.0

Materias

Machine learning - (MACHINE LEARNING) Family resemblance - (FAMILY RESEMBLANCE) Bayesian networks - (BAYESIAN NETWORKS) CIENCIAS FÍSICO MATEMÁTICAS Y CIENCIAS DE LA TIERRA - (CTI) MATEMÁTICAS - (CTI) CIENCIA DE LOS ORDENADORES - (CTI)

Resumen o descripción

The introduction of the notion of family resemblance represented a major shift in Wittgenstein’s thoughts on the meaning of words, moving away from a belief that words were well defined, to a view that words denoted less well defined categories of meaning. This paper presents the use of the notion of family resemblance in the area of machine learning as an example of the benefits that can accrue from adopting the kind of paradigm shift taken by Wittgenstein. The paper presents a model capable of learning exemplars using the principle of family resemblance and adopting Bayesian networks for a representation of exemplars. An empirical evaluation is presented on three data sets and shows promising results that suggest that previous assumptions about the way we categories need reopening.

Editor

Springer Science+Business Media

Fecha de publicación

2008

Tipo de publicación

Artículo

Versión de la publicación

Versión publicada

Recurso de información

http://inaoe.repositorioinstitucional.mx/jspui/handle/1009/1043

Formato

application/pdf

Idioma

Inglés

Audiencia

Estudiantes

Investigadores

Público en general

Sugerencia de citación

Vadera, S., et al., (2008). Using wittgenstein’s family resemblance principle to learn exemplars, Found Sci (13):67–74

Repositorio Orígen

Repositorio Institucional del INAOE

Descargas

372