Titlebook: Artificial Neural Networks - ICANN 2010; 20th International C Konstantinos Diamantaras,Wlodek Duch,Lazaros S. Il Conference proceedings 201

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https://doi.org/10.1007/978-3-662-52764-1 to neural networks provides a statistically justified subspace method of classification. The underlying structural mixture model includes binary structural parameters and can be optimized by EM algorithm in full generality. Formally, the structural model reduces the number of parameters included an

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https://doi.org/10.1007/978-3-662-52764-1 quasi-Hebbian expansion where each pattern is supplied with its own individual weight. For such matrices statistical physics methods allow one to derive an equation describing local minima of the functional. A model where only one weight differs from other ones is discussed in details. In this case

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https://doi.org/10.1007/978-3-662-52764-1. Such architectures, however, achieve state-of-the-art results on low-resolution machine vision tasks such as recognition of handwritten characters. We have adapted the inherent multi-level parallelism of CNNs for Nvidia’s CUDA GPU architecture to accelerate the training by two orders of magnitude.

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Optical Detectors and Receiverser, the differences between those models makes a comparison of the properties of different aggregation functions hard. Our aim is to gain insight into different functions by directly comparing them on a fixed architecture for several common object recognition tasks. Empirical results show that a max