%0 Conference Paper %1 semi-supervised.text.li.2004 %A Li, Maokuan %A Cheng, Yusheng %A Zhao, Honghai %B CGIV '04: Proceedings of the International Conference on Computer Graphics, Imaging and Visualization %C Washington, DC, USA %D 2004 %I IEEE Computer Society %K classification data semi-supervised text unlabeled using %P 183--186 %R http://dx.doi.org/10.1109/CGIV.2004.53 %T Unlabeled Data Classification via Support Vector Machines and k-means Clustering %U http://ieeexplore.ieee.org/Xplore/login.jsp?url=/iel5/9243/29306/01323982.pdf?temp=x %X Support vector machines(SVMS), a powerful machine method developed from statistical learning and have made significant achievement in some field. Introduced in the early 90ýs, 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 oneýs 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. %@ 0-7695-2178-9

@inproceedings{semi-supervised.text.li.2004, abstract = {Support vector machines(SVMS), a powerful machine method developed from statistical learning and have made significant achievement in some field. Introduced in the early 90ýs, 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 oneýs 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.}, added-at = {2009-03-07T09:11:49.000+0100}, address = {Washington, DC, USA}, author = {Li, Maokuan and Cheng, Yusheng and Zhao, Honghai}, biburl = {https://www.bibsonomy.org/bibtex/2ffc5e8461286cfb2b6d905ab46f1c2b3/huiyangsfsu}, booktitle = {CGIV '04: Proceedings of the International Conference on Computer Graphics, Imaging and Visualization}, doi = {http://dx.doi.org/10.1109/CGIV.2004.53}, interhash = {9906aa07edc87015a58cee86e8dc0442}, intrahash = {ffc5e8461286cfb2b6d905ab46f1c2b3}, isbn = {0-7695-2178-9}, keywords = {classification data semi-supervised text unlabeled using}, pages = {183--186}, publisher = {IEEE Computer Society}, timestamp = {2009-03-07T09:11:49.000+0100}, title = {Unlabeled Data Classification via Support Vector Machines and k-means Clustering}, url = {http://ieeexplore.ieee.org/Xplore/login.jsp?url=/iel5/9243/29306/01323982.pdf?temp=x}, year = 2004 }