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Kernel Methods for Minimum Entropy Encoding
Found in: Machine Learning and Applications, Fourth International Conference on
By Stefano Melacci,Marco Gori
Issue Date:December 2011
pp. 352-357
Following the basic principles of Information-Theoretic Learning (ITL), in this paper we propose Minimum Entropy Encoders (MEEs), a novel approach to data clustering. We consider a set of functions that project each input point onto a minimum entropy confi...
 
Semi-supervised clustering using similarity neural networks
Found in: Neural Networks, IEEE - INNS - ENNS International Joint Conference on
By Stefano Melacci, Marco Maggini, Lorenzo Sarti
Issue Date:June 2009
pp. 2065-2072
Similarity Neural Networks (SNNs) are a novel neural network model designed to learn similarity measures for pairs of patterns, exploiting binary supervision. SNNs guarantee to compute non negative and symmetric measures, and show good generalization capab...
 
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