Abstract

Symbolic Machine Learning systems and applications, especially when applied to real-world domains, must face the problem of concepts that cannot be captured by a single denition, but require several alternate definitions, each of which covers part of the full concept extension. This problem is particularly relevant for incremental systems, where progressive covering approaches are not applicable, and the learn- ing and refinement of the various definitions is interleaved during the learning phase. In these systems, not only the learned model depends on the order in which the examples are provided, but it also depends on the choice of the specific definition to be refined. This paper proposes different strategies for determining the order in which the alternate definitions of a concept should be considered in a generalization step, and evaluates their performance on a real-world domain dataset

Autore Pugliese

• Ferilli Stefano

Tutti gli autori

• PAZIENZA A.;ESPOSITO F.;FERILLI S.

Titolo volume/Rivista

Non Disponibile

Anno di pubblicazione

2015

ISSN

0302-9743

ISBN

978-331921541-6

Numero di citazioni Wos

Nessuna citazione

Ultimo Aggiornamento Citazioni

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Numero di citazioni Scopus

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Ultimo Aggiornamento Citazioni

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Settori ERC

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Codici ASJC

Non Disponibile