Titlebook: Artificial Neural Networks and Machine Learning -- ICANN 2012; 22nd International C Alessandro E. P. Villa,W?odzis?aw Duch,Günther Pal Conf

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Bacterial fermentation of meats,lection using autocorrelation analysis for each day of the week and build a separate prediction model using linear regression and backpropagation neural networks. We used two years of 5-minute electricity load data for the state of New South Wales in Australia to evaluate performance. Our results sh

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https://doi.org/10.1007/978-3-642-83425-7in order to modify the hypothesis space, and to speed-up learning and processing times. We study two kinds of filters that are known to be computationally efficient in feed-forward processing: fused convolution/sub-sampling filters, and separable filters. We compare the complexity of the back-propag

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https://doi.org/10.1007/978-3-319-48646-8s work has focused on uncovering connections among scalar random variables. We generalize existing methods to apply to collections of multi-dimensional random ., focusing on techniques applicable to linear models. The performance of the resulting algorithms is evaluated and compared in simulations,

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Effective Actions and?Anomalieser space the parameters of the model are represented by fewer parameters, and hence training can be faster. After training, the parameters of the model can be generated from the parameters in compressed parameter space. We show that for supervised learning, learning the parameters of a model in comp