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http://inaoe.repositorioinstitucional.mx/jspui/handle/1009/1043| Using wittgenstein’s family resemblance principle to learn exemplars | |
| ANDRES FLORENCIO RODRIGUEZ MARTINEZ LUIS ENRIQUE SUCAR SUCCAR Jia Wu | |
| Acceso Abierto | |
| Atribución-NoComercial-SinDerivadas | |
| Machine learning Family resemblance Bayesian networks | |
| 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. | |
| Springer Science+Business Media | |
| 2008 | |
| Artículo | |
| Inglés | |
| Estudiantes Investigadores Público en general | |
| Vadera, S., et al., (2008). Using wittgenstein’s family resemblance principle to learn exemplars, Found Sci (13):67–74 | |
| CIENCIA DE LOS ORDENADORES | |
| Versión publicada | |
| publishedVersion - Versión publicada | |
| Aparece en las colecciones: | Artículos de Ciencias Computacionales |
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