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 e-book 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. construction at the luck of the former six successive ISNN symposiums, ISNN has turn into a well-established sequence of renowned and high quality 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 different components. these days, the sector of neural networks has been fostered a long way past the conventional man made neural networks. This yr, ISNN 2010 acquired 591 submissions from greater than forty international locations and areas. according to rigorous reports, one hundred seventy papers have been chosen for booklet within the lawsuits. The papers gathered within the court cases hide a large spectrum of fields, starting from neurophysiological experiments, neural modeling to extensions and purposes of neural networks. we now have geared up the papers into volumes in accordance with their issues. the 1st quantity, entitled “Advances in Neural Networks- ISNN 2010, half 1,” covers the next issues: neurophysiological beginning, concept and versions, studying and inference, neurodynamics. the second one quantity en- tled “Advance in Neural Networks ISNN 2010, half 2” covers the subsequent 5 themes: SVM and kernel tools, imaginative and prescient and picture, info 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

Example text

By applying a fuzzy logic in SVR, different input data points contribute differently to the optimization of regression function [9]. Bao et al. employed FSVR in financial time series forecasting and achieved high performance in stock composition index [10]. 2 Software Defect Prediction Techniques Software metric is a function whose inputs are software codes and whose output is a single numerical value that can be interpreted as the degree to which software possesses a given attribute that affects its quality, and it is also called software complexity or quality metric.

I , Σj ) = ((φ(Σi ) · φ(Σj )) = ||Σi . 1/2 means the component-wise extraction of matrices, and D is the degree of the polynomial-matrix kernel function. Theorem. When each subset has one sample, the polynomial kernel function based on the data vector equals to twice of the polynomial-matrix one based on the autocorrelation matrix. That means the degree d is the twice of the degree D. Proof. When each subset contains one sample, the autocorrelation matrix Σi = xi xT i . Using the polynomial-matrix kernel function, it follows: T T D κ(Σi , Σj ) = ||Σi .

25 ). 5 Performance Criteria The two performance criteria engaged in this paper to evaluate the effects of software defect prediction are mean squared error (MSE) and squared correlation coefficient (denoted as r2 ) as follows: M SE = and r2 = 4 1 n n (f (xi ) − yi )2 , n [n (3) j=1 n n [n i=1 f (xi )yi − i=1 f (xi ) i=1 yi ]2 n n n 2 2 2 i=1 f (xi ) − ( i=1 f (xi )) ][n i=1 yi − ( n i=1 yi )2 ] . (4) Experiments and Discussion Table 2 shows the experimental results when we treat the whole set as the training set.

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