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 gather refereed papers offered 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 renowned and top of the range meetings on neural computation and its purposes. ISNN goals at supplying a platform for scientists, researchers, engineers, in addition to scholars to assemble jointly to give and speak about the most recent progresses in neural networks, and purposes in diversified parts. these days, the sector of neural networks has been fostered some distance past the normal man made neural networks. This yr, ISNN 2010 obtained 591 submissions from greater than forty nations and areas. in line with rigorous studies, a hundred and seventy papers have been chosen for booklet within the complaints. The papers accrued within the court cases conceal a extensive spectrum of fields, starting from neurophysiological experiments, neural modeling to extensions and purposes of neural networks. we've got prepared the papers into volumes in keeping with their subject matters. the 1st quantity, entitled “Advances in Neural Networks- ISNN 2010, half 1,” covers the next issues: neurophysiological beginning, 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 subject matters: SVM and kernel equipment, imaginative and prescient and picture, 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
This can help developers allocate limited developing resources to modules containing more defects. In this paper, we propose a novel method of using Fuzzy Support Vector Regression (FSVR) in predicting software defect numbers. Fuzziﬁcation input of regressor can handle unbalanced software metrics dataset. Compared with the approach of support vector regression, the experiment results with the MIS and RSDIMU datasets indicate that FSVR can get lower mean squared error and higher accuracy of total number of defects for modules containing large number of defects.
2, it could also be observed that the smaller deviations were made by T-SVR than those made by BPNN at most time. However, there were several points where the deviations made by T-SVR were too large. These points caused the large average errors that could be observed from Table 3. Fig. 2. Graphical presentation of different models 5 Conclusions In this study, an effective SVR model with ACO had been applied to predict grid resources. Compared to T-SVR model, the ACO-SVR model provided higher prediction precision and spent even less time on parameters selection.
Of the 3rd International Workshop on Predicator Models in Software Engineering (May 2007) 18. R. ): Handbook of Software Reliability Engineering. IEEE Computer Society Press and McGraw-Hill Book Company (1996) 19. : An empirical study on testing and fault tolerance for software reliability engineering. In: Proc. of the 14th IEEE International Symposium on Software Reliability Engineering (ISSRE 2003), November 2003, pp. 119–130 (2003) 20. : Software metrics analysis with genetic algorithm and aﬃnity propagation clustering.