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软间隔组合凸线性感知器设计

, PP. 924-934

Keywords: 组合凸线性感知器,软间隔,K均值,泛化能力,分片线性分类器

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Abstract:

组合凸线性感知器是用来构造分片线性分类器的一个通用理论框架。对于凸可分和叠可分情况,分别使用支持凸线性感知器算法和支持组合凸线性感知器算法将两类样本分开。在此基础上,文中提出一种软间隔的组合凸线性感知器设计方法。该方法首先映射原空间数据到高维特征空间,然后利用K均值算法将其中一类样本聚类成多个簇,并在每一簇与另一类样本间构造凸线性感知器,最后集成组合凸线性感知器。该方法能解决原感知器模型不适用非叠可分数据的问题,并且在一定程度上简化模型结构,在保证分类精度的前提下,提高泛化能力。实验结果证实文中方法的有效性,同其它分片线性分类器的对比也说明了它的优势。

References

[1]  Gai Kun, Zhang Changshui. Learning Discriminative Piecewise Li near Models with Boundary Points / / Proc of the 24th AAAI Confer ence on Artificial Intelligence. Georgia, USA, 2010: 444-450
[2]  Cai Bingbing, Huang Tong, Zhuang Xinhua, et al. Piecewise Li near Classifiers Using Binary Tree Structure and Genetic Algorithm.Pattern Recognition, 1996, 29(11): 1905-1917
[3]  Kostin A. A Simple and Fast MultiClass Piecewise Linear Pattern Classifier. Pattern Recognition, 2006, 39(11): 1949-1962
[4]  Fukunage K. Introduction to Statistical Pattern Recognition. San Diego, USA: Academic Press, 1993
[5]  Li Yujian, Liu Bo, Yang Xinwu, et al. Multiconlitron: A General Piecewise Linear Classifier. IEEE Trans on Neural Networks,2011, 22(2): 276-289
[6]  Cortes C, Vapnik V. Support Vector Networks. Machine Learning,1995, 20(3): 273-297
[7]  Vapnik V. The Nature of Statistical Learning Theory. New York,USA: SpringerVerlag, 1995
[8]  Friess T T, Harrison R. Support Vector Neural Networks: The Ker nel Adatron with Bias and SoftMargin. Technical Report, ACSE TR725. Sheffield, UK: University of Sheffield, 1998
[9]  Keerthi S S, Shevade S K, Bhattacharyya C, et al. A Fast Iterative Nearest Point Algorithm for Support Vector Machine Classifier De sign. IEEE Trans on Neural Networks, 2000, 11(1): 124-136
[10]  Franc V, Hlavac V. An Iterative Algorithm Learning the Maximal Margin Classifier. Pattern Recognition, 2003, 36 (9): 1985-1996
[11]  Hartigan J A, Wong M A. A KMeans Clustering Algorithm.Applied Statistics, 1979, 28(1): 100-108
[12]  Bagirov A M. MaxMin Separability. Optimization Methods and Software, 2005, 20(2/3): 277-296
[13]  Bagirov A M, Ugon J, Webb D. An Efficient Algorithm for the Incremental Construction of a Piecewise Linear Classifier. Informa tion Systems, 2011, 36(4): 782-790
[14]  Bagirov A M, Ugon J, Webb D, et al. Classification through Incremental MaxMin Separability. Pattern Analysis and Applica tions, 2011, 14(2): 165-174
[15]  Cover T M, Hart P E. Nearest Neighbor Pattern Classificatio
[16]  Webb D. Efficient Piecewise Linear Classifiers and Applications.Ph. D Dissertation. Ballarat, Australia: University of Ballarat,2010932 模式识别与人工智能摇摇摇26 卷
[17]  Mangasarian O L. Multisurface Method of Pattern Separation. IEEETrans on Information Theory, 1968, 14(6): 801-807
[18]  Herman G T, Yeung K T D. On PiecewiseLinear Classification.IEEE Trans on Pattern Analysis and Machine Intelligence, 1992,14(7): 782-786
[19]  Sklansky J, Michelotti L. Locally Trained Piecewise Linear Classi fiers. IEEE Trans on Pattern Analysis and Machine Intelligence,1980, 2(2): 101-111
[20]  Park Y, Sklansky J. Automated Design of MultipleClass Piecewise Linear Classifiers / / Proc of the 9th International Conference on Pa ttern Recognition. Rome, Italy, 1988, II: 1068-1071
[21]  Tenmoto H, Kudo M, Shimbo M. Piecewise Linear Classifiers with an Appropriate Number of Hyperplanes. Pattern Recognition, 1998,31(11): 1627-1634

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