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Penang, Malaysia
July 2, 2004 to July 2, 2004
ISBN: 0-7695-2178-9
pp: 183-186
Maokuan Li , Navy Submarine Academy
Yusheng Cheng , Navy Submarine Academy
Honghai Zhao , Navy Submarine Academy
Support vector machines(SVMS), a powerful machine method developed from statistical learning and have made significant achievement in some field. Introduced in the early 90?s, they led to an explosion of interest in machine learning. However, like most machine learning algorithms, they are generally applied using a selected training set classified in advance. With the repaid development of the internet and telecommunication, huge of information has been produced as digital data format, generally the data is unlabeled. It is impossible to classify the data with one?s own hand one by one in many realistic problems, so that the research on unlabeled data classification has been grown. Improvements in databases technology, computing performance and artificial intelligence have contributed to the development of intelligent data analysis. In this paper, a SVMs classifier based on k-means algorithm is presented for the classification of unlabeled data.
Support Vector Machines, Data Mining, k-means clustering
Maokuan Li, Yusheng Cheng, Honghai Zhao, "Unlabeled Data Classification via Support Vector Machines and k-means Clustering", CGIV, 2004, Proceedings. International Conference on Computer Graphics, Imaging and Visualization, Proceedings. International Conference on Computer Graphics, Imaging and Visualization 2004, pp. 183-186, doi:10.1109/CGIV.2004.1323982
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