Tuesday, March 1st 2016
Title: Consensus prototype learning
According to a Chinese proverb “Three cobblers combined equal the master mind”. Combining multiple models into one consensus model helps among others reduce the uncertainty in the initial models. Consensus prototype learning can be formulated in arbitrary problem domains, either in an informal or a formal manner. In this talk the focus will be given to the formal framework of so-called generalized median. Related computation algorithms will be presented. A variety of applications in pattern recognition will be shown to demonstrate the power of consensus prototype learning, including consensus clustering, median strings, median graphs, image segmentation combination, and atlas construction.
Colloquium coordinators are Prof.dr. M. Aiello (e-mail :
Prof.dr. M. Biehl (e-mail:
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