Wednesday, March 25th 2015
Univ. of Applied Sciences, Mittweida
Learning interpretable models from Dissimilarity Data
LVQ (Learning Vector Quantization) methods are frequently used
algorithms in the context of classication in the area of machine learning.
combine many useful features such as sparse and interpretable models. However,
these models are only suitable for vectorial data. The existing extensions on
dissimilarity data are not longer interpretable or sparse. One method to keep
the properties is the so-called median algorithm.
In this talk I will give a brief overview of LVQ methods and their extensions
to dissimilarity data. I will name advantages and disadvantages of these methods
and motivate the introduction of so-called median methods. A brief overview
of the functionality of median methods and the advantages and disadvantages
of these will be given. It will be also shown how the median algorithms can be
applied to a wide variety of problem settings using illustrative examples.
Colloquium coordinators are Prof.dr. M. Aiello (e-mail :
Prof.dr. M. Biehl (e-mail:
This year, the University of Groningen has submitted four research projects to compete for the national Klokhuis Science Prize. The aim of this prize is to introduce a young and wide audience to academic research. The winning project will be...
Het project WIJS, het initiatief dat de Gemeente Groningen en de Hanzehogeschool in 2014 startten, is uitgebreid met vier partners: WIJ-Groningen, de Rijksuniversiteit Groningen, Alfa-college en Noorderpoort. Vandaag wordt de komst van de vier...
Non-executive directors (hereafter: directors) have to take a critical stance towards the top managers they supervise. This has been the dominant perspective among researchers and the media after the financial crisis of 2008 and recent major...