PUB - Publikationen an der Universität Bielefeld

Due to the increasing amount of large data sets, efficient learning algorithms are necessary. Also the interpretation of the final model is desirable to draw efficient conclusions from the model results. Prototype based learning algorithms have been extended recently to proximity learners to analyze data given in non-standard data formats. The supervised methods of this type are of special interest but suffer from a large number of optimization parameters to model the prototypes. In this contribution we derive an efficient core set based preprocessing to restrict the number of model parameters to O(nϵ2) with n as the number of prototypes. Accordingly, the number of model parameters gets independent of the size of the data sets but scales with the requested precision ε of the core sets. Experimental results show that our approach does not significantly degrade the performance while significantly reducing the memory complexity.