Publikation: Towards learning in parallel universes
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Most learning algorithms operate in a clearly defined feature space and assume that all relevant structure can he found in this one, single space. For many local learning methods, especially the ones working on distance metrics (e.g. clustering algorithms), this poses a serious limitation. We disucss an algorithm that directly finds a set of cluster centers based on an analysis of the distribution of patterns in the local neighborhood of each potential cluster center through the use of so-called Neighborgrams. This type of cluster construction makes it feasable to find clusters in several feature spaces in parallel, effectively finding the optimal feature space for each cluster independently. We demonstrate how the algorithm works on an artificial data set and show its usefulness using a well-known benchmark data set.
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BERTHOLD, Michael R., David E. PATTERSON, 2004. Towards learning in parallel universes. 2004 IEEE International Conference on Fuzzy Systems. Budapest, Hungary. In: 2004 IEEE International Conference on Fuzzy Systems (IEEE Cat. No.04CH37542). IEEE, 2004, pp. 67-71. ISBN 0-7803-8353-2. Available under: doi: 10.1109/FUZZY.2004.1375689BibTex
@inproceedings{Berthold2004Towar-5466, year={2004}, doi={10.1109/FUZZY.2004.1375689}, title={Towards learning in parallel universes}, isbn={0-7803-8353-2}, publisher={IEEE}, booktitle={2004 IEEE International Conference on Fuzzy Systems (IEEE Cat. No.04CH37542)}, pages={67--71}, author={Berthold, Michael R. and Patterson, David E.} }
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