會議論文
| 學年 | 86 |
|---|---|
| 學期 | 2 |
| 發表日期 | 1998-05-04 |
| 作品名稱 | Genetic-algorithms-based approach to self-organizing feature map and its application in cluster analysis |
| 作品名稱（其他語言） | |
| 著者 | Su, Mu-chun; Chang, Hsiao-te |
| 作品所屬單位 | 淡江大學電機工程學系 |
| 出版者 | Institute of electrical and electronics engineers (IEEE) |
| 會議名稱 | Neural Networks Proceedings, 1998. IEEE World Congress on Computational Intelligence. The 1998 IEEE International Joint Conference on |
| 會議地點 | Anchorage, AK, USA |
| 摘要 | In the traditional form of the self-organizing feature map (SOFM) algorithm, the criterion for stopping training is either to terminate the training procedure when no noticeable changes in the feature map are observed or to stop training when the number of iterations reaches a prespecific number. In this paper we propose an efficient method for measuring the degree of topology preservation. Based on the method we apply genetic algorithms (GAs) in two stages to form a topologically ordered feature map. We then use a special method to interpret a SOFM formed by the proposed GA-based method to estimate the number and the locations of clusters from a multidimensional data set without labeling information. Two data sets are used to illustrate the performance of the proposed methods |
| 關鍵字 | |
| 語言 | en |
| 收錄於 | |
| 會議性質 | 國際 |
| 校內研討會地點 | |
| 研討會時間 | 19980504~19980509 |
| 通訊作者 | 蘇木春 |
| 國別 | USA |
| 公開徵稿 | Y |
| 出版型式 | |
| 出處 | Neural Networks Proceedings, 1998. IEEE World Congress on Computational Intelligence. The 1998 IEEE International Joint Conference on (Volume:1 ), pp.735-740 |
| 相關連結 |
機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/39003 ) |