Titlebook: Artificial Neural Nets and Genetic Algorithms; Proceedings of the I David W. Pearson,Nigel C. Steele,Rudolf F. Albrech Conference proceedin

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APEX

Acclaim

A learning probabilistic neural network with fuzzy inference,roposed. The advantages of this network lie in the possibility of classification of the data with substantially overlapping clusters, and tuning of the activation function parameters improves the accuracy of classification. Simulation results confirm the efficiency of the proposed approach in the data classification problems.

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MERIT

Reinforced Search in Stochastic Neural Network,t. Reinforcement signal from environment is used for weights and variance adaptation. This is experimentally compared with more traditional techniques like gradient-based learning algorithm and evolutionary algorithm.

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Vertical Vector Fields and Neural Networks: An Application in Atmospheric Pollution Forecasting,to determine zones in the input space that are mapped onto the same output, they act in a similar way to kernels of linear mappings but in a nonlinear setting. In the paper we illustrate our ideas using data from a real application, namely forecasting atmospheric pollution for the town of Saint-Etienne in France.

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Illegible Rage: Performing Femininity in We adapt a boosting algorithm to the problem of predicting future values of time series, using recurrent neural networks as base learners. The experiments we performed show that boosting actually provides improved results and that the weighted median is better for combining the learners than the weighted mean.

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