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A Galois Framework for the Study of Analogical Classifiers

Miguel Couceiro 1 Erkko Lehtonen 2 
1 ORPAILLEUR - Knowledge representation, reasonning
LORIA - NLPKD - Department of Natural Language Processing & Knowledge Discovery
2 CMA - Centro de Matematica e Aplicaçoes
DM - Departamento de Matemática
Abstract : In this paper, we survey some recent advances in the study of analogical classifiers, i.e., classifiers that are compatible with the principle of analogical inference. We will present a Galois framework induced by relation between formal models of analogy and the corresponding classes of analogy preserving functions. The usefulness these general results will be illustrated over Boolean domains, which explicitly present the Galois closed sets of analogical classifiers for different pairs of formal models of Boolean analogies.
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Submitted on : Thursday, August 4, 2022 - 9:47:35 AM
Last modification on : Friday, August 5, 2022 - 3:31:07 AM


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  • HAL Id : hal-03745365, version 1



Miguel Couceiro, Erkko Lehtonen. A Galois Framework for the Study of Analogical Classifiers. IJCAI-ECAI Workshop on the Interactions between Analogical Reasoning and Machine Learning (IARML@IJCAI-ECAI 2022), Miguel Couceiro; Pierre-Alexandre Murena, Jul 2022, Vienna, Austria. pp.51-61. ⟨hal-03745365⟩



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