By Guosheng Hu, Liang Hu, Jing Song, Pengchao Li, Xilong Che, Hongwei Li (auth.), Liqing Zhang, Bao-Liang Lu, James Kwok (eds.)
This booklet and its sister quantity acquire refereed papers provided on the seventh Inter- tional Symposium on Neural Networks (ISNN 2010), held in Shanghai, China, June 6-9, 2010. development at the good fortune of the former six successive ISNN symposiums, ISNN has develop into a well-established sequence of well known and top of the range meetings on neural computation and its functions. ISNN goals at offering a platform for scientists, researchers, engineers, in addition to scholars to assemble jointly to provide and speak about the newest progresses in neural networks, and purposes in various parts. these days, the sector of neural networks has been fostered some distance past the normal synthetic neural networks. This yr, ISNN 2010 bought 591 submissions from greater than forty nations and areas. in keeping with rigorous stories, one hundred seventy papers have been chosen for booklet within the lawsuits. The papers accrued within the court cases disguise a huge spectrum of fields, starting from neurophysiological experiments, neural modeling to extensions and functions of neural networks. now we have prepared the papers into volumes in accordance with their issues. the 1st quantity, entitled “Advances in Neural Networks- ISNN 2010, half 1,” covers the next themes: neurophysiological beginning, thought and versions, studying and inference, neurodynamics. the second one quantity en- tled “Advance in Neural Networks ISNN 2010, half 2” covers the subsequent 5 subject matters: SVM and kernel tools, imaginative and prescient and picture, information mining and textual content research, BCI and mind imaging, and applications.
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Additional info for Advances in Neural Networks - ISNN 2010: 7th International Symposium on Neural Networks, ISNN 2010, Shanghai, China, June 6-9, 2010, Proceedings, Part II
Applied ANNs to grid resources prediction successfully. Experimental results showed the ANN approach provided an improved prediction over that of NWS. However, ANNs have some drawbacks such as hard to pre-select the system architecture, spending much training time, and lacking knowledge representation facilities. In 1995, support vector machine (SVM) was developed by Vapnik  to provide better solutions than ANNs. SVM can solve classification problems (SVC) and regression problems (SVR) successfully and effectively.
I − 1 M Σ). 1 M Σi . ∗ (Σi − ∗Σ− 1 M Σ. 1 D M Σ)||B ∗ Σi + 1 M2 Σ. ∗ Σ)||D B (12) = (K − IM K − KIM + IM KIM )ij Therefore, the centering kernel matrix K = K − IM K − KIM + IM KIM , where IM = (1/M)M×M . An Improved Kernel Principal Component Analysis for Large-Scale Data Set 13 3 Experimental Results and Discussion Some experiments were performed to demonstrate the effectiveness of the proposed method. In order to differentiate from the standard KPCA, we abbreviate the method 1order-KPCA, which means Kernel Principal Component Analysis based on 1-order statistical quantity and shorten the method 2order-KPCA, which means Kernel Principal Component Analysis based on 2-order statistical quantity.
6 G. Hu et al. 3 Experimental Results Firstly, the results of parameters selection were shown. Fig. 1 illustrated the correlation curves of ACO-SVR model for the optimal fitness versus the generation number. From Fig. 1, it was obvious that the MSEcv of the optimal fitness decreased with the increase of generation number. When the sample evolution reached Generation 62, the MSEcv of five-fold cross validation converged, indicating that the searching of the ACO was featured with quite excellent efficiency.
Advances in Neural Networks - ISNN 2010: 7th International Symposium on Neural Networks, ISNN 2010, Shanghai, China, June 6-9, 2010, Proceedings, Part II by Guosheng Hu, Liang Hu, Jing Song, Pengchao Li, Xilong Che, Hongwei Li (auth.), Liqing Zhang, Bao-Liang Lu, James Kwok (eds.)