Full-Class Set Classification Using the Hungarian Algorithm

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• Dr. Ludmila Kuncheva, School of Computer Science, Bangor University
• Monday 01 November 2010, 16:00-17:00
• UG40, School of Computer Science.

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.

This talk is included in these lists:

• Artificial Intelligence and Natural Computation seminars
• Computer Science Departmental Series
• Computer Science Distinguished Seminars
• UG40, School of Computer Science
• computer sience

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