Download Advances in Neural Networks - ISNN 2010: 7th International by Guosheng Hu, Liang Hu, Jing Song, Pengchao Li, Xilong Che, PDF

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By Guosheng Hu, Liang Hu, Jing Song, Pengchao Li, Xilong Che, Hongwei Li (auth.), Liqing Zhang, Bao-Liang Lu, James Kwok (eds.)

This ebook and its sister quantity acquire refereed papers awarded on the seventh Inter- tional Symposium on Neural Networks (ISNN 2010), held in Shanghai, China, June 6-9, 2010. development at the luck of the former six successive ISNN symposiums, ISNN has develop into a well-established sequence of well known and high quality meetings on neural computation and its purposes. ISNN goals at offering a platform for scientists, researchers, engineers, in addition to scholars to collect jointly to give and speak about the most recent progresses in neural networks, and purposes in diversified parts. these days, the sphere of neural networks has been fostered a ways past the normal synthetic neural networks. This 12 months, ISNN 2010 acquired 591 submissions from greater than forty international locations and areas. in line with rigorous studies, a hundred and seventy papers have been chosen for book within the court cases. The papers accumulated within the lawsuits hide a vast spectrum of fields, starting from neurophysiological experiments, neural modeling to extensions and purposes of neural networks. we have now prepared the papers into volumes according to their subject matters. the 1st quantity, entitled “Advances in Neural Networks- ISNN 2010, half 1,” covers the next themes: neurophysiological origin, thought and versions, studying and inference, neurodynamics. the second one quantity en- tled “Advance in Neural Networks ISNN 2010, half 2” covers the next 5 subject matters: SVM and kernel equipment, imaginative and prescient and photo, info mining and textual content research, BCI and mind imaging, and applications.

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Extra resources for Advances in Neural Networks - ISNN 2010: 7th International Symposium on Neural Networks, ISNN 2010, Shanghai, China, June 6-9, 2010, Proceedings, Part II

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Com Abstract. Accurate grid resources prediction is crucial for a grid scheduler. In this study, support vector regression (SVR), which is an effective regression algorithm, is applied to grid resources prediction. In order to build an effective SVR model, SVR’s parameters must be selected carefully. Therefore, we develop an ant colony optimization-based SVR (ACO-SVR) model that can automatically determine the optimal parameters of SVR with higher predictive accuracy and generalization ability simultaneously.

Multilayer feedforward networks are universal approximations. Neural Networks, 336–359 (1989) 14. : Learning Internal Representations by Error Propagation in Parallel Distributed Processing. MIT Press, Cambridge (1986) 15. : International and Business Forecasting Methods. cn Abstract. To deal with the computational and storage problem for the large-scale data set, an improved Kernel Principal Component Analysis based on 1-order and 2-order statistical quantity, is proposed. By dividing the large scale data set into small subsets, we could treat 1-order and 2-order statistical quantity (mean and autocorrelation matrix) of each subset as the special computational unit.

As shown in Table 1, we can see that the original metrics dataset does not meet this assumption. Principal Components Analysis (PCA) is a method to perform de-correlation and reduce data dimensionality; what is more, previous research [13,20] has verified that the usage of the first few principal components perform good in static-metrics-based software defect prediction. We choose the first two principal components (PCA2) as input of regression training. 2 Fuzzy Membership Function There are 308 modules out of MIS whose defect numbers are not greater than 10, and the other modules’ defect numbers range from 11 to 98, and in RSDIMU, 20 Z.

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