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Investigating machine learning methods based on concepts of computational geometry

Abstract

Inductive machine learning is a very successful automatic learning model that has been largely implemented using many different formalisms and techniques. This project aims at the investigation of algorithms that belong to a specific class of machine learning techniques, the algorithms based on concepts related to computational geometry, particularly those based on the linear separability, convex hull and spanning trees. (AU)

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