Titlebook: Artificial Neural Networks and Machine Learning - ICANN 2011; 21st International C Timo Honkela,W?odzis?aw Duch,Samuel Kaski Conference pro

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Hybrid Parallel Classifiers for Semantic Subspace Learning,are the performance of our hybrid architecture with a single classifier and show that it outperforms the single classifier system by a large margin when tested with a variety of hybrid combinations. Our results show that subspace classification accuracy is boosted and learning time reduced significantly with this new hybrid architecture.

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Image Receptive Fields Neural Networks for Object Recognition,dom receptive fields in the image space. These . (IRF-NN) show remarkable performances for recognition applications, with extremely fast learning, and can be applied directly to images without pre-processing.

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The Local and Modular Fermat Problem,of collective activity. This is performed by adopting a relatively complex dynamic synaptic model. Some light is shed on the relevance of the usage of the developed framework to mimic complex cortical functions, e.g. content-addressable memory.

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https://doi.org/10.1007/978-3-7091-8634-3s, represents a pathologic neuron. We numerically solved the non-linear Poisson-Boltzmann equation, by considering the densities of charges dissolved in an electrolytic solution and fixed on both glycocalyx and cytoplasmic proteins. We found important differences among the potential profiles of the two cells.

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Alcoholic beverage fermentations,ental Cholesky factorization in calculating corrections. By computer experiments, we show that the proposed method is comparable to or faster than SMO (Sequential minimum optimization) using the second order information.