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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Wenjiang Zhou Yin Xu Lunwen Wang Ling Zhang Ying Tan |
| Copyright Year | 2007 |
| Description | Author affiliation: Electron. Eng. Inst., Hefei (Wenjiang Zhou; Yin Xu; Lunwen Wang) |
| Abstract | The constructive neural networks based on the covering algorithms is suitable for large-scale data mining because it can be local processing and has little computational complexity, however, the local processing lows classification precision. In this paper, covering algorithms is firstly extended to kernel covering algorithms and we secondly construct a kind of finite mixture probabilistic model based on kernel covering algorithms according to the probability meaning of Gaussian function and finally introduce the global optimizing computation by "maximum likelihood theory", realize the global optimization problem of the covering algorithms so as to expand the range of application of covering algorithms and improve its precision. The results of the experiment are given as an example to illustrate the validation of the method. |
| Starting Page | 24 |
| Ending Page | 28 |
| File Size | 305513 |
| Page Count | 5 |
| File Format | |
| ISBN | 9780769528755 |
| DOI | 10.1109/ICNC.2007.66 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2007-08-24 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Support vector machines Neural networks Neurons Pattern classification Support vector machine classification Artificial neural networks Fuzzy neural networks Large-scale systems Data mining Kernel |
| Content Type | Text |
| Resource Type | Article |
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