Titlebook: Artificial Neural Networks and Machine Learning -- ICANN 2013; 23rd International C Valeri Mladenov,Petia Koprinkova-Hristova,Nikola K Conf

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A Distributed Learning Algorithm Based on Frontier Vector Quantization and Information Theoryenetic algorithm. The results obtained from twelve classification data sets demonstrate the efficacy of the proposed method. In average, the distributed FVQIT performs 13.56 times faster than the FVQIT and improves classification accuracy by 5.25%.

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Learning with Hard Constraintsh provides a description of the “optimal body of the agent”, i.e. the functional structure of the solution to the proposed learning problem. It is shown that the solution can be represented in terms of a set of “support constraints”, thus extending the well-known notion of “support vectors”.

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Artificial Neural Networks and Machine Learning -- ICANN 2013978-3-642-40728-4Series ISSN 0302-9743 Series E-ISSN 1611-3349

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Fernsehaneignung und Alltagsgesprächethe system to utilise memory efficiently, and superimposed distributed representations in order to reduce the time complexity of a tree search to .(.), where . is the depth of the tree. This new work reduces the memory required by the architecture, and can also further reduce the time complexity.

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