| dc.creator (作者) | 張洺偉;林智仁;翁久幸 | zh_TW |
| dc.creator (作者) | Chang, Ming-Wei;Lin, Chih-Jen;Weng, Ruby C. | - |
| dc.date (日期) | 2004-05 | en_US |
| dc.date.accessioned | 19-十二月-2008 14:56:55 (UTC+8) | - |
| dc.date.available | 19-十二月-2008 14:56:55 (UTC+8) | - |
| dc.date.issued (上傳時間) | 19-十二月-2008 14:56:55 (UTC+8) | - |
| dc.identifier.uri (URI) | https://nccur.lib.nccu.edu.tw/handle/140.119/18226 | - |
| dc.description.abstract (摘要) | We present a framework for the unsupervised segmentation of switching dynamics using support vector machines. Following the architecture by Pawelzik et al., where annealed competing neural networks were used to segment a nonstationary time series, in this paper, we exploit the use of support vector machines, a well-known learning technique. First, a new formulation of support vector regression is proposed. Second, an expectation-maximization step is suggested to adaptively adjust the annealing parameter. Results indicate that the proposed approach is promising. | - |
| dc.format | application/pdf | en_US |
| dc.format.extent | 319500 bytes | - |
| dc.format.mimetype | application/pdf | - |
| dc.language | en | en_US |
| dc.language | en-US | en_US |
| dc.language.iso | en_US | - |
| dc.relation (關聯) | IEEE Transactions on Neural Networks 15(3),720-727 | en_US |
| dc.title (題名) | Analysis of Switching Dynamics with Competing Support Vector Machine | en_US |
| dc.type (資料類型) | article | en |
| dc.identifier.doi (DOI) | 10.1109/IJCNN.2002.1007515 | - |
| dc.doi.uri (DOI) | http://dx.doi.org/10.1109/IJCNN.2002.1007515 | - |
