University of Birmingham > Talks@bham > Artificial Intelligence and Natural Computation seminars > Full-Class Set Classification Using the Hungarian Algorithm

Full-Class Set Classification Using the Hungarian Algorithm

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If you have a question about this talk, please contact Leandro Minku.

Consider a set-classification task where c ob jects must be labelled simultaneously in c classes, knowing that there is only one ob ject coming from each class (full-class set). Such problems may occur in automatic attendance registration systems, simultaneous tracking of fast moving ob jects 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.

This talk is part of the Artificial Intelligence and Natural Computation seminars series.

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