Evgueni N. Smirnov

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We address the problem of applying machine-learning classi-fiers in domains where incorrect classifications have severe consequences. In these domains we propose to apply classifiers only when their performance can be defined by the domain expert prior to classification. The classifiers so obtained are called reliable classifiers. In the article we present(More)
Instance retraction is a difficult problem for concept learning by version spaces. In this paper, two new version-space representations are introduced: instance-based maximal boundary sets and instance-based minimal boundary sets. They are correct representations for the class of admissible concept languages and are efficiently computable. Compared to other(More)
This paper presents an incremental concept learning approach to identi¯cation of concepts with high overall accuracy. The main idea is to address the concept overlap as a central problem when learning multiple descriptions. Many traditional inductive algorithms, as those from the disjunctive version space family considered here, face this problem. The(More)
In this paper we consider the open problem how to unify version-space representations. We present a first solution to this problem, namely a new version-space representation called adaptable boundary sets (ABSs). We show that a version space can have a space of ABSs representations. We demonstrate that this space includes the boundary-set representation and(More)