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dc:title "HTML Summary of #2124 \n\nSolving Multiple-Instance Problem: A Lazy Learning Approach\n\n";
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bibo:abstract "As opposed to traditional supervised learning, multiple-instance learning \n concerns the problem of classifying a bag of instances, given bags that are \n labeled by a teacher as being overall positive or negative. Current research \n mainly concentrates on adapting traditional concept learning to solve this \n problem. In this paper we investigate the use of lazy learning and Hausdorff \n distance to approach the multiple-instance problem. We present two variants of \n the K-nearest neighbor algorithm, called Bayesian-KNN and Citation-KNN, solving \n the multiple-instance problem. Experiments on the Drug discovery benchmark data \n show that both algorithms are competitive with the best ones conceived in the \n concept learning framework. Further work includes exploring of a combination of \n lazy and eager multiple-instance problem classifiers."^^xsd:string;
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skos:prefLabel "Artificial Intelligence" .
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skos:prefLabel "Machine Learning" .
foaf:name "Morgan Kaufmann"^^xsd:string;
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