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

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. construction at the good fortune of the former six successive ISNN symposiums, ISNN has develop into a well-established sequence of renowned and high quality meetings on neural computation and its functions. ISNN goals at delivering 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 varied parts. these days, the sector of neural networks has been fostered some distance past the normal synthetic neural networks. This 12 months, ISNN 2010 got 591 submissions from greater than forty nations and areas. in accordance with rigorous reports, a hundred and seventy papers have been chosen for book within the lawsuits. The papers gathered within the court cases disguise a wide spectrum of fields, starting from neurophysiological experiments, neural modeling to extensions and functions of neural networks. we now have prepared the papers into volumes in accordance with their subject matters. the 1st quantity, entitled “Advances in Neural Networks- ISNN 2010, half 1,” covers the subsequent subject matters: neurophysiological origin, concept and types, studying and inference, neurodynamics. the second one quantity en- tled “Advance in Neural Networks ISNN 2010, half 2” covers the subsequent 5 issues: SVM and kernel tools, imaginative and prescient and photograph, info mining and textual content research, BCI and mind imaging, and applications.

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In: Proc. of the 16th IEEE International Symposium on Software Reliability Engineering (ISSRE 2005), November 2005, pp. 213–222 (2005) 13. : Support vector machines for regression and applications to software quality prediction. In: Alexandrov, V. ) ICCS 2006. LNCS, vol. 3994, pp. 781–788. Springer, Heidelberg (2006) 14. : Automating algorithms for the identification of fault-prone files. In: Proc. of International Symposium on Software Testing and Analysis, July 2007, pp. 219–227 (2007) 15. : Predicting the location and number of faults in large software systems.

Resource Prediction System (RPS) [2] is a project in which grid resources are modeled as linear time series process. Multiple conventional linear models are evaluated, including AR, MA, ARMA, ARIMA and ARFIMA models. Their results show that the simple AR model is the best model of this class because of its good predictive power and low overhead. With the development of artificial neural networks (ANNs), ANNs have been successfully employed for modeling time series. [3] and Eswaradass et al. [4] L.

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.

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