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Titlebook: Neural Networks and Artificial Intelligence; 8th International Co Vladimir Golovko,Akira Imada Conference proceedings 2014 Springer Interna

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A Learning Technique for Deep Belief Neural Networkstion square error. The main contribution of this paper is new interpretation of training rules for restricted Boltzmann machine. It is shown that traditional approach for restricted Boltzmann machine training is particular case of proposed technique. We demonstrate the efficiency of proposed approach using deep nonlinear auto-encoder.
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A Hybrid Genetic Algorithm and Radial Basis Function NEATin the non- Markovian double pole balancing without velocity and the car racing strategy. It is shown that the proposed technique significantly outperforms classical neuroevolution techniques in both of the above benchmark problems.
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1865-0929 14, held in Brest, Belarus, in June 2014. The 19 revised full papers presented were carefully reviewed and selected from 27 submissions. The papers are organized in topical sections on forest resource management; artificial intelligence by neural networks; optimization; classification; fuzzy approac
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Can Artiticial Neural Networks Evolve to be Intelligent Like Human?intelligence, how can we measure it, how those measurements really represent an intelligence, and so on. For the purpose, we will take a brief look at a couple of formal definitions of machine intelligence so far proposed. We also take it a consideration on our own definition of machine intelligence.
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Quality Evaluation of E-commerce Sites Based on Adaptive Neural Fuzzy Inference System an effective tool for the quality analysis process modelling of the given type of sites. It also shows that the convenient and powerful tool is much better than the traditional artificial neural network for the simulation of sites evaluation.
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