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)
Preface When I was searching for a thesis assignment, I looked for a topic involving a multiagent system. During my Knowledge Engineering studies, I had investigated and experimented with several multiagent systems: the challenges of such systems were intriguing and fascinating. Therefore, I wanted to implement such a multiagent system myself. My M.Sc.(More)
This paper proposes a new approach to classification reliability. The key idea is to maintain version spaces containing (close approximations of) the target classifiers. In this way the unanimous-voting rule applied on these version spaces outputs reliable instance classifications. Version spaces are defined in a hypothesis space of oriented hyperplanes.(More)