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 offered 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 top quality 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 offer 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 conventional synthetic neural networks. This yr, ISNN 2010 obtained 591 submissions from greater than forty nations and areas. in accordance with rigorous studies, one hundred seventy papers have been chosen for ebook within the lawsuits. The papers gathered within the court cases disguise a extensive 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 according to their subject matters. the 1st quantity, entitled “Advances in Neural Networks- ISNN 2010, half 1,” covers the next issues: neurophysiological starting place, idea and types, 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 snapshot, information mining and textual content research, BCI and mind imaging, and applications.

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Extra 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

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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.

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. Yan, X. Chen, and P. Guo Table 1. Detailed description of metrics in MIS and RSDIMU Dataset Both Metrics Detailed Description LOC Number of lines of code COM LOC Number of lines of comments, named TComm in MIS SLOC Number of lines of source code, named CL in MIS N1 , N2 , n1 , n2 Halstead’s software metrics, corresponding to N and N’ in MIS MIS RSDIMU TChar Number of characters MChar Number of comment characters DChar Number of code characters NF Jensen’s estimate of program length metric V(G) McCabe’s cyclomatic complexity metric BW Belady’s bandwidth measure COM RAT The ratio of COM LOC to LOC NSC Number of sub-level classes NTM Number of top-level classes TCOM RAT The ratio of COM LOC to SLOC 167 files are less than 4, the other files range from 4 to 14.

Keywords: Fuzzy support vector regression, Software defect prediction, Software metrics. 1 Introduction Software defect, commonly defined as deviations from expectation that might lead to software failures [1], is one of the most important problems in software engineering. Software engineers always want to identify which software modules contain more defects so that those modules would be paid more attention during the testing period. Knowing how many defects in each module is also a pressing problem to tell whether a project is on schedule.

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