Full-class set classification using the Hungarian algorithm
Int. J. Machine Learning & Cybernetics(2010)
摘要
Consider a set-classification task where c objects must be labelled simultaneously in c classes, knowing that there is only one object coming from each class (full-class set). Such problems may occur in automatic attendance registration systems, simultaneous tracking of fast moving objects and more. A Bayes-optimal solution to the full-class set classification problem is proposed using a single classifier and the Hungarian assignment algorithm. The advantage of set classification over individually based classification is demonstrated both theoretically and experimentally, using simulated, benchmark and real data.
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关键词
Full-class set classification,Bayes-optimal classifier,Label assignment problem
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