Analyzing Big Data with the Hybrid Interval Regression Methods
Big data is a new trend at present, forcing the significant impacts on information technologies. In big data applications, one of the most concerned issues is dealing with large-scale data sets that often require computation resources provided by public cloud services. How to analyze big data effici...
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| Format: | Article |
| Language: | English |
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Wiley
2014-01-01
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| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2014/243921 |
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| _version_ | 1849695595828084736 |
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| author | Chia-Hui Huang Keng-Chieh Yang Han-Ying Kao |
| author_facet | Chia-Hui Huang Keng-Chieh Yang Han-Ying Kao |
| author_sort | Chia-Hui Huang |
| collection | DOAJ |
| description | Big data is a new trend at present, forcing the significant impacts on information technologies. In big data applications, one of the most concerned issues is dealing with large-scale data sets that often require computation resources provided by public cloud services. How to analyze big data efficiently becomes a big challenge. In this paper, we collaborate interval regression with the smooth support vector machine (SSVM) to analyze big data. Recently, the smooth support vector machine (SSVM) was proposed as an alternative of the standard SVM that has been proved more efficient than the traditional SVM in processing large-scale data. In addition the soft margin method is proposed to modify the excursion of separation margin and to be effective in the gray zone that the distribution of data becomes hard to be described and the separation margin between classes. |
| format | Article |
| id | doaj-art-d068681cb7634018aebbfb6d397f86e3 |
| institution | DOAJ |
| issn | 2356-6140 1537-744X |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-d068681cb7634018aebbfb6d397f86e32025-08-20T03:19:43ZengWileyThe Scientific World Journal2356-61401537-744X2014-01-01201410.1155/2014/243921243921Analyzing Big Data with the Hybrid Interval Regression MethodsChia-Hui Huang0Keng-Chieh Yang1Han-Ying Kao2Department of Business Administration, National Taipei University of Business, No. 321, Section 1, Jinan Road, Zhongzheng District, Taipei City 100, TaiwanDepartment of Information Management, Hwa Hsia Institute of Technology, No. 111, Gongzhuan Road, Zhonghe District, New Taipei City 235, TaiwanDepartment of Computer Science and Information Engineering, National Dong Hwa University, No. 123, Hua-Shi Road, Hualien 97063, TaiwanBig data is a new trend at present, forcing the significant impacts on information technologies. In big data applications, one of the most concerned issues is dealing with large-scale data sets that often require computation resources provided by public cloud services. How to analyze big data efficiently becomes a big challenge. In this paper, we collaborate interval regression with the smooth support vector machine (SSVM) to analyze big data. Recently, the smooth support vector machine (SSVM) was proposed as an alternative of the standard SVM that has been proved more efficient than the traditional SVM in processing large-scale data. In addition the soft margin method is proposed to modify the excursion of separation margin and to be effective in the gray zone that the distribution of data becomes hard to be described and the separation margin between classes.http://dx.doi.org/10.1155/2014/243921 |
| spellingShingle | Chia-Hui Huang Keng-Chieh Yang Han-Ying Kao Analyzing Big Data with the Hybrid Interval Regression Methods The Scientific World Journal |
| title | Analyzing Big Data with the Hybrid Interval Regression Methods |
| title_full | Analyzing Big Data with the Hybrid Interval Regression Methods |
| title_fullStr | Analyzing Big Data with the Hybrid Interval Regression Methods |
| title_full_unstemmed | Analyzing Big Data with the Hybrid Interval Regression Methods |
| title_short | Analyzing Big Data with the Hybrid Interval Regression Methods |
| title_sort | analyzing big data with the hybrid interval regression methods |
| url | http://dx.doi.org/10.1155/2014/243921 |
| work_keys_str_mv | AT chiahuihuang analyzingbigdatawiththehybridintervalregressionmethods AT kengchiehyang analyzingbigdatawiththehybridintervalregressionmethods AT hanyingkao analyzingbigdatawiththehybridintervalregressionmethods |