Alexander A. Frolov, Dušan Húsek, Pavel Yu. Polyakov's Advances in Neural Networks – ISNN 2012: 9th International PDF

By Alexander A. Frolov, Dušan Húsek, Pavel Yu. Polyakov (auth.), Jun Wang, Gary G. Yen, Marios M. Polycarpou (eds.)

ISBN-10: 3642313450

ISBN-13: 9783642313455

ISBN-10: 3642313469

ISBN-13: 9783642313462

ISBN-10: 3642313612

ISBN-13: 9783642313615

ISBN-10: 3642313620

ISBN-13: 9783642313622

The two-volume set LNCS 7367 and 7368 constitutes the refereed lawsuits of the ninth foreign Symposium on Neural Networks, ISNN 2012, held in Shenyang, China, in July 2012. The 147 revised complete papers provided have been conscientiously reviewed and chosen from a variety of submissions. The contributions are based in topical sections on mathematical modeling; neurodynamics; cognitive neuroscience; studying algorithms; optimization; development reputation; imaginative and prescient; photo processing; info processing; neurocontrol; and novel applications.

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Extra info for Advances in Neural Networks – ISNN 2012: 9th International Symposium on Neural Networks, Shenyang, China, July 11-14, 2012. Proceedings, Part I

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Assigning protein functions by comparative genome analysis: protein phylogenetic profiles. Proceedings of the National Academy of Sciences of the United States of America 96(8), 4285 (1999) 6. : Comparative assessment of large-scale data sets of protein–protein interactions. Nature 417(6887), 399–403 (2002) 7. : Hierarchical organization of modularity in metabolic networks. Science 297(5586), 1551 (2002) 8. : The KEGG databases at GenomeNet. Nucleic Acids Research 30(1), 42 (2002) Pruning Feedforward Neural Network Search Space Using Local Lipschitz Constants Zaiyong Tang1, Kallol Bagchi2, Youqin Pan1, and Gary J.

Keywords: Feature reduction, Mutual information, Extreme learning machines, Spectral data. 1 Introduction Feature selection has been addressed to solve the “curse of dimensionality” problems when modeling with high dimensional spectral data [1,2], which can avoid overfitting, resist noise and strengthen prediction performance. Genetic algorithm-partial least square (GA-PLS) using for feature selection has been applied on many spectral data sets, which shows better result [3]. As the random initialization of the GA, the feature selection process has to be performed many times.

Com Abstract. In this paper, a hierarchical neural network with cascading architecture is proposed and its application to classification is analyzed. This cascading architecture consists of multiple levels of neural network structure, in which the outputs of the hidden neurons in the higher hierarchical level are treated as an equivalent input data to the input neurons at the lower hierarchical level. The final predictive result is obtained through a modified weighted majority vote scheme. In this way, it is hoped that new patterns could be learned from hidden layers at each level and thus the combination result could significantly improve the learning performance of the whole system.

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Advances in Neural Networks – ISNN 2012: 9th International Symposium on Neural Networks, Shenyang, China, July 11-14, 2012. Proceedings, Part I by Alexander A. Frolov, Dušan Húsek, Pavel Yu. Polyakov (auth.), Jun Wang, Gary G. Yen, Marios M. Polycarpou (eds.)


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