基于特权信息的SVM模型研究及应用
Research and Application of SVM Model Based on Privileged Information
DOI: 10.12677/AAM.2017.69150, PDF,  被引量    国家自然科学基金支持
作者: 曹 颖, 王芬艳, 卢常景：中国地质大学数学与物理学院，湖北 武汉
关键词: 特权信息；支持向量机；应用；Privilege Information； Support Vector Machine (SVM)； Application
摘要: 当使用支持向量机(Support Vector Machines, SVM)进行分类时，可能会遇到训练样本中存在额外信息的情况。它是不可忽视的，因为它会严重影响测试样本的分类准确率。本文提出了一种基于特权信息的SVM模型，其包含了特权信息分布的多种情况，能有效地解决多种特权信息分布问题。本文首先叙述了SVM分类的基本原理，在简单介绍特权信息概念后，提出了基于特权信息的SVM模型及其特例。接着介绍了基于特权信息的SVM模型应用，最后对该研究领域存在的问题及发展方向进行了总结。
Abstract: When using Support Vector Machine (SVM) to training classification model, we may encounter ad-ditional information in training samples. Because it will seriously affect classification accuracy of test samples, it really can’t be ignored. In this paper, a SVM model based on privilege information is proposed, which contains many cases of privilege information and can solve many kinds of dis-tribution problems effectively. This paper first described the basic principles of SVM classification, and then it introduced the concept of privilege information. Next this paper proposed a SVM model based on privilege information and gave its special cases. Then, the application of SVM model based on privilege information was introduced. Finally, the existing problems and the development direction of this research were summarized.
文章引用：曹颖, 王芬艳, 卢常景. 基于特权信息的SVM模型研究及应用[J]. 应用数学进展, 2017, 6(9): 1248-1254. https://doi.org/10.12677/AAM.2017.69150

参考文献

[1] Vapnik, V.N. (1998) Statistical Learning Theory. John Wiley & Sons, Hoboken.
[2] 熊平. 数据挖掘算法与Clementine实践[M]. 北京: 清华大学出版社, 2011.
[3] Bishop, C.M. (2006) Pattern Recognition and Machine Learning. Springer, Berlin.
[4] 朱明. 数据挖掘[M]. 合肥: 中国科学技术大学出版社, 2002.
[5] Burnaev, E. and Smolyakov, D. (2016) One-Class SVM with Privileged Information and Its Application to Malware Detection. ArXiv, Los Alamos.
[6] Gine, E. and Koltchinskii, V. (2006) Concentration Inequalities and Asymptotic Results for Ratio Type Empirical Processes. Annals of Probability, 34, 1143-1216.
[Google Scholar] [CrossRef] 
[7] 邓乃扬, 田英杰. 数据挖掘的新方法—支持向量机[M]. 北京: 科学出版社, 2004.
[8] Manevitz, L.M. and Yousef, M. (2001) One-Class SVMs for Document Classification. Journal of Machine Learning Research, 2, 139-154.
[9] Vapnik, V. and Vashist, A. (2009) A New Learning Paradigm: Learning Using Privileged Information. Neural Networks, 22, 544-557.
[Google Scholar] [CrossRef] [PubMed]
[10] Boser, G. (1992) A Training Algorithm for Optimal Margin Classifiers. Proceedings of the Fifth Annual Workshop on Computational Learning Theory, Pittsburgh, 27-29 July 1992, 144-152.
[Google Scholar] [CrossRef] 
[11] Cristianini, N., Shawe-Taylor, J. 支持向量机导论[M]. 李国正, 王猛, 曾华军, 译. 北京: 电子工业出版社, 2004.
[12] Pechyony, D. and Vapnik, V. On the Theory of Learning with Privileged Information. John Wiley & Sons, Hoboken.
[13] Boucheron, S., Bousquet, O. and Lugosi, G. (2005) Theory of Classification: A Survey of Some Recent Advances. ESAIM: Probability and Statistics, 9, 329-375.
[Google Scholar] [CrossRef] 
[14] Cortes, C. and Vapnik, V. (1995) Support-Vector Networks. Machine Learning, 20, 273-297.
[Google Scholar] [CrossRef] 
[15] Schölkopf, B., Smola, A.J., Williamson, R.C. and Bartlett, P.L. (2000) New Support Vector Algorithms. Neural Computation, 12, 1207-1245.
[Google Scholar] [CrossRef] [PubMed]
[16] Micchelli, C. and Pontil, M. (2005) Learning the Kernel Function via Regularization. Journal of Machine Learning Research, 6, 1099-1125.

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