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Analogical Classifiers: A Theoretical Perspective

Abstract : In recent works, analogy-based classifiers have been proved quite successful. They exhibit good accuracy rates when compared with standard classification methods. Nevertheless, a theoretical study of their predictive power has not been done so far. One of the main barriers has been the lack of functional definition: analogical learners have only algorithmic definitions. The aim of our paper is to complement the empirical studies with a theoretical perspective. Using a simplified framework, we first provide a concise functional definition of the output of an analogical learner. Two versions of the definition are considered, a strict and a relaxed one. As far as we know, this is the first definition of this kind for analogical learner. Then, taking inspiration from results in k-NN studies, we examine some analytic properties such as convergence and VC-dimension, which are among the basic markers in terms of machine learning expressiveness. We then look at what could be expected in terms of theoretical accuracy from such a learner, in a Boolean setting. We examine learning curves for artificial domains, providing experimental results that illustrate our formulas, and empirically validate our functional definition of analogical classifiers.
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Submitted on : Wednesday, May 2, 2018 - 9:59:27 AM
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  • HAL Id : hal-01782594, version 1
  • OATAO : 18959


Nicolas Hug, Henri Prade, Gilles Richard, Mathieu Serrurier. Analogical Classifiers: A Theoretical Perspective. European Conference on Artificial Intelligence (ECAI 2016), Aug 2016, La Hague, France. pp. 689-697. ⟨hal-01782594⟩



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