全部 标题 作者
关键词 摘要

OALib Journal期刊
ISSN: 2333-9721
费用:99美元

查看量下载量

相关文章

更多...

半监督型广义特征值最接近支持向量机*

, PP. 349-353

Keywords: 支持向量机,半监督学习,流形学习

Full-Text   Cite this paper   Add to My Lib

Abstract:

广义特征值最接近支持向量机(GEPSVM)是近年提出的一种两分类方法.本文结合GEPSVM的平面特点和流形学习,给出一类半监督学习算法SemiGEPSVM.该方法不仅仍保持对诸如XOR问题的分类能力,而且在每类仅有一个有标样本的极端情形下,仍具有适用性.当已标样本不能用于构建超平面时,本文采用k-近邻方法选择样本并标记类别.一旦已标样本的个数可构建超平面时,采用本文的选择方法标记样本.此外,本文还从理论上证明该算法存在全局最优解.最后,SemiGEPSVM算法的有效性在人工数据集和标准数据集上得到验证.

References

[1]  Fung G, Mangasarian O L. Proximal Support Vector Machine Classifiers // Proc of the 7th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. San Francisco, USA, 2001: 77-86
[2]  Mangasarian O L, Wild E W. Multisurface Proximal Support Vector Machine Classification via Generalized Eigenvalues. IEEE Trans on Pattern Analysis and Machine Intelligence, 2006, 28(1): 69-74
[3]  Jayadeva, Khemchandai R, Chandra S. Fuzzy Proximal Support Vector Classification via Generalized Eigenvalues // Proc of the 1st International Conference on Pattern Recognition and Machine Intelligence. Kolkata, India, 2005: 360-363
[4]  Jayadeva, Khemchandai R, Chandra S. Fuzzy Multi-Category Proximal Support Vector Classification via Generalized Eigenvalues. Soft Computing-A Fusion of Foundations, Methodologies and Applications, 2007, 11(7): 679-685
[5]  Jayadeva, Khemchandai R, Chandra S. Twin Support Vector Machines for Pattern Classification. IEEE Trans on Pattern Analysis and Machine Intelligence, 2007, 29(5): 905-910
[6]  Li Haifeng, Jiang Tao, Zhang Keshu. Efficient and Robust Feature Extraction by Maximum Margin Criterion. IEEE Trans on Neural Networks, 2006, 17(1): 157-165
[7]  Yang Xubing, Chen Songcan. Proximal Support Vector Machine Based on Prototypal Multiclassification Hyperplanes. Journal of Computer Research and Development, 2006, 43(10): 1700-1705 (in Chinese) (杨绪兵,陈松灿.基于原型超平面的多类最接近支持向量机.计算机研究与发展, 2006, 43(10): 1700-1705)
[8]  Zhou Zhihua, Zhan Dechuan, Yang Qiang. Semi-Supervised Learning with Very Few Labeled Training Examples // Proc of the 22nd AAAI Conference on Artificial Intelligence. Vancouver, Canada, 2007: 675-680
[9]  Zhou Dengyong, Weston J, Gretton A. Ranking on Data Manifolds // Thrun S, Saul L K, Schlkopf B, eds. Advances in Neural Information Processing Systems. Cambridge, USA: MIT Paress, 2004, 16: 169-176
[10]  Zhang Dengyong, Zhou Zhihua, Chen Songcan. Semi-Supervised Dimensionality Reduction // Proc of the 7th SIAM International Conference on Data Mining. Minneapolis, USA, 2007: 629-634
[11]  Bradley P S, Mangasarian O L. k-Plane Clustering. Journal of Global Optimization, 2000, 16(1): 23-32

Full-Text

Contact Us

service@oalib.com

QQ:3279437679

WhatsApp +8615387084133