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Because of it is applicability in various field, multiinstance learning or multiinstance problem becoming more popular in
machine learning research field. Different from supervised learning, multiinstance learning related to the problem of classifying an
unknown bag into positive or negative label such that labels of instances of bags are ambiguous. This paper uses and study three
different knearest neighbor algorithm namely Bayesian kNN, citation kNN and Bayesian Citation kNN algorithm for solving multiinstance
problem. Similarity between two bags is measured using Hausdroff distance. To overcome the problem of false positive
instances constructive covering algorithm used. Also the problem definition, learning algorithm and experimental data sets related to
multiinstance learning framework are briefly reviewed in this paper
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