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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Wang Zhuo Chu Lili |
| Copyright Year | 2010 |
| Description | Author affiliation: College of business administration, Liaoning Technical University, HuLuDao (Wang Zhuo; Chu Lili) |
| Abstract | Support Vector Machine (SVM) is a new technology of classification in data mining, which is a small sample of statistical learning theory based on structural risk minimization principle and VC theory. It has simple structure and good classification ability, but its processing speed is slow when we deal with large amount of data, affecting classification performance. In order to overcome the shortcoming that SVM is better adaptability, combining rough sets of attribute reduction algorithm with SVM method of classification, the paper presents a new algorithm of text classification based on rough set and support vector machine. In a certain extent of support vector machines (SVM) to improve the ability of processing large-scale data of support vector machine, and through the simulation experiments to verify the superiority and adaptability of algorithm. |
| File Size | 219890 |
| File Format | |
| ISBN | 9781424458219 |
| e-ISBN | 9781424458240 |
| DOI | 10.1109/ICFCC.2010.5497769 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2010-05-21 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Virtual colonoscopy Statistical learning Classification algorithms Data mining classification Support vector machines support vector machine Text categorization Support vector machine classification Rough sets rough set Large-scale systems Risk management |
| Content Type | Text |
| Resource Type | Article |
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