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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Conference proceedings 2012n Lausanne, Switzerland, in September 2012. The 162 papers included in the proceedings were carefully reviewed and selected from 247 submissions. They are organized in topical sections named: theoretical neural computation; information and optimization; from neurons to neuromorphism; spiking dynamic

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Simplifying ConvNets for Fast Learningation algorithm on ConvNets based on these different kinds of filters. We show that using these filters allows to reach the same level of recognition performance as with classical . for handwritten digit recognition, up to 3.3 times faster.

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A Modified Artificial Fish Swarm Algorithm for the Optimization of Extreme Learning Machines generalization performance. The algorithm presents the basic . (AFSA) and some features from . (Crossover and Mutation) to improve the quality of the solutions during the search process. The results of the simulations demonstrated good generalization capacity from the best individuals obtained in the training phase.

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Retracted: Robust Training of Feedforward Neural Networks Using Combined Online/Batch Quasi-Newton Tconcept. Neural network training is presented to demonstrate the validity of combined algorithm. The algorithm achieves more robust training and accurate generalization results than other quasi-Newton based training algorithms.

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,Fermented meats — A world perspective,earning step is smaller than optimum value ... When it is larger than .., it decreases slower with the simple method, and the residual error is larger than with the true gradient descent method. Moreover, when there is output noise, .. is no longer optimum; thus, the simple method is not robust in noisy circumstances.