Get 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 complaints 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 rigorously reviewed and chosen from a number of submissions. The contributions are based in topical sections on mathematical modeling; neurodynamics; cognitive neuroscience; studying algorithms; optimization; trend popularity; imaginative and prescient; photo processing; details processing; neurocontrol; and novel applications.

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Read Online or Download Advances in Neural Networks – ISNN 2012: 9th International Symposium on Neural Networks, Shenyang, China, July 11-14, 2012. Proceedings, Part I PDF

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Additional 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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Linguistic Models as a Framework of User-Centric System Modeling. IEEE Trans. 36, 727–745 (2006) 6. : A fuzzy ensemble of parallel polynomial neural networks with information granules formed by fuzzy clustering. Knowledge-Based Systems 23, 202–219 (2010) 7. : Design of K-means clustering-based polynomial radial basis function neural networks (pRBF NNs) realized with the aid of particle swarm optimization and differential evolution. cn Abstract. Effective modeling based on the high dimensional data needs feature selection and fast learning speed.

1 Introduction Classification has been one of the key focuses of machine learning and data mining for several decades. Recent research suggested that ensemble learning could be an important technique to improve the classification performance of many base learning algorithms, such as neural network, decision tree, K-nearest neighbor, and others. This work was supported in part by the National Science Foundation under Grant ECCS 1053717 and the K. C. Wong Education Foundation, Hong Kong. J. G. M.

They proposed to use statistical analysis to reduce the number of input variables, hence reducing the search space. Rempis and Pasemann suggested using constrained modularization to remove large parts of the search space. However, their focus is on the network topology rather than the weight space [9]. In this paper, we focus on the property of the search space. We show that an FNN is Lipschitzian. , [10]) can be applied to neural network training. Lipschitz constants can be used in a branch-andbound framework to compute lower bounds on the optimal solution within a current subset of weights [11].

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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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