Titlebook: Artificial Neural Networks - ICANN 2008; 18th International C Véra Kůrková,Roman Neruda,Jan Koutník Conference proceedings 2008 Springer-Ve

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Improving the Learning Speed in 2-Layered LSTM Network by Estimating the Configuration of Hidden Unifor function approximation tasks. The motivation of this method is based on the behavior of the hidden units and the complexity of the function to be approximated. The results obtained for 1-D and 2-D functions show that the proposed methodology improves the network performance, stabilizing the trai

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OP-ELM: Theory, Experiments and a Toolboxgression and classification problems. The results are compared with widely known Multilayer Perceptron (MLP) and Least-Squares Support Vector Machine (LS-SVM) methods. As the experiments (regression and classification) demonstrate, the OP-ELM methodology is considerably faster than the MLP and the L

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Quadratically Constrained Quadratic Programming for Subspace Selection in Kernel Regression Estimatired models in the context of reproducing kernel Hilbert spaces. In this setting the task of input selection is converted into the task of selecting functional components depending on one (or more) inputs. In turn the process of learning with embedded selection of such components can be formalized as

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Aus der Vorgeschichte des Zeppelins,We present a training method which adjusts the weights of the MLP (Multilayer Perceptron) to preserve the distance invariance in a low dimensional space. We apply visualization techniques to display the detailed representations of the trained neurons.

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