Advances in Neural Networks – ISNN 2012: 9th International by Alexander A. Frolov, Dušan Húsek, Pavel Yu. Polyakov
By Alexander A. Frolov, Dušan Húsek, Pavel Yu. Polyakov (auth.), Jun Wang, Gary G. Yen, Marios M. Polycarpou (eds.)
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 offered have been conscientiously reviewed and chosen from a number of submissions. The contributions are dependent in topical sections on mathematical modeling; neurodynamics; cognitive neuroscience; studying algorithms; optimization; trend attractiveness; imaginative and prescient; snapshot processing; details 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
Although this method can obtain best feature sub-set, it is time-consuming, especially for high dimensional spectral data. Therefore, a simple method based on MIFS was proposed in . It is used in here: given a threshold value of the MI, if the MI values of the features are higher than threshold value, it is selected. However, how to select the optimal threshold value is a difficulty problem. 2 Nonlinear Modeling Based on Kernel ELM ELM was originally proposed for the single-hidden layer feed forward networks (SLFNs) [7,10] and then extended to the generalized single-hidden layer feed forward network [11,12].
Thus for the criterion function F, we have a Lipschitz constant of 1 2 LF = max (t pk − o pk ) 2 Lok . p k k (4) Extension of the procedure to estimating a Lipschitz constant for an FNN with more than one hidden layer can be carried out by applying the basic lemmas recursively, as illustrated above. 4 Computing Local Lipschitz Constant The procedures outlined in Section 3 allow us to easily compute Lipschitz constant over subsets of the weight space. For clarity of exposition, we will consider computing the Lipschitz constant of a three layer FNN with a single output unit.
More simulations will be done to validate this approach further. Acknowledgments. 20100471464). References 1. : Unsupervised Linear Feature-Extraction Methods and Their Effects in the Classification of High-Dimensional Data. IEEE Transaction on Geoscience and Remote Sensing 45, 469–483 (2007) 2. : Feature Selection with Kernel Class Separability. IEEE Transactions on Pattern Analysis and Machine Intelligence 30, 1534–1546 (2008) 3. : Variable Selection for Multivariate Calibration Using a Genetic Algorithm: Prediction of Additive Concentrations in Polymer Films from Fourier Transform-infrared Spectral Data.