西安交通大学计算机科学与技术系,西安,710049
网络首发:2012-08-10,
纸质出版:2012
移动端阅览
赵金伟 1, 冯博琴 1, 闫桂荣 2. 泛化的统一切比雪夫多项式核函数[J]. 西安交通大学学报, 2012,46(8):43-48.
Generalized Uniform Chebyshev Polynomial Kernel[J]. 2012, 46(8): 43-48.
针对分布稀疏、特征不明显的小样本数据回归中的属性冗余问题
基于统一切比雪夫多项式
提出了一种向量形式输入的可变正交多项式核函数——泛化的统一切比雪夫多项式核函数.新的核函数通过利用统一切比雪夫多项式的正交性和可变性扩大了函数的搜索空间
通过调整多项式阶数有效地控制了特征空间维数
从而解决了稀疏数据回归中的属性冗余问题.另外
利用Mercer定理证明了该核函数的有效性.在多组标准数据集和实际工程数据集上对核函数的性能进行了实验对比
结果证明新的核函数预测精度较高
泛化能力较好
在大多数标准数据集上的性能优于其他切比雪夫多项式核函数.
Based on a group of unified Chebyshev polynomials(UCP)
a new kernel for vector inputs
named generalized uniform Chebyshev polynomial kernel(GUCK)
is proposed to solve the problem of redundant attributes in the regression analysis on small-scale data sets. The proposal kernel can extend the search space of optimal kernel function by the orthogonality and adaptivity of UCP and control the dimension of the feature space by adjusting the polynomial coefficient of UCP. The problem of redundant attributes is settled by this method. Moreover
the proposal kernel
GUCK
has been proved that it is a valid support vector machine(SVM)kernel. The simulation results and application results show that GUCK can lead to better generalization performance in comparison with other common kernels
and is well applicable to the practical dataset. The GUCK has an advantage over other Chebyshev kernels on the majority of benchmark data sets
VAPNIK V N. The nature of statistical learning theory [M]. Dusseldorf, Germany: Springer-Verlag, 1995.
SUYKENS J A K, VANDEWALLE J. Least squares support vector machine classifiers [J]. Neural Processing Letters, 1999, 9(3):293-300.
FUNG G, MANGASARIAN O L. Proximal support vector machine classifiers [C]∥Proc of the 7th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York,USA: ACM, 2001:77-86.
HUANG H P, LIU Y H. Fuzzy support vector machines for pattern recognition and data mining [J]. International Journal of Fuzzy Systems, 2002, 4(3):826-835.
LEE K Y, DAE-WON K. Possibilistic support vector machines [J]. Pattern Recognition, 2005,38(8):1325-1327.
ROSTAMIZADEH A. Theoretical foundations and algorithms for learning with multiple kernels[D].New York, USA: New York University, 2010.
KLOFT M, BREFELD U, SONNENBURG S, et al. Non-sparse regularization for multiple kernel learning, machine learning group[J]. Franklinstr, 2010, 28(29):6-9.
CORTES C, MOHRI M, ROSTAMIZADEH A. Two-stage learning kernel algorithms [C]∥Proc of the 27th International Conference on Machine Learning. Corvallis, USA. ICML,2010:239-246.
CRISTIANINI N, KANDOLA J, ELISSEEF A, et al. On kernel target alignment [C]∥Proc of the Neural Information Processing Systems. Cambridge, MA, USA: MIT Press, 2001: 367-373.
KANDOLA J, TAYLOR J S, CRISTIANINI N. Optimizing kernel alignment over combinations of kernels, Neuro COLT Technical Report NC-TR-02-121[R].London, UK: University of London, 2002.
HOLMES D E, JAIN L C. Innovations in machine learning [M]. Berlin, Germany: Springer-Verlag, 2006:205-256.
SONNENBURG S, RAETSCH G, SCHAEFER C, et al. Large scale multiple kernel learning [J]. Journal of Machine Learning Research, 2006, 7(7):1531-1565.
RAKOTOMAMONJY A, BACH F, CANU S, et al. More efficiency in multiple kernel learning[C]∥Proc of the 24th International Conference on Machine Learning. Corvallis, USA: ICML, 2007:775-782.
ZHAO Jinwei, FENG Boqin, YAN Guirong, et al. The unified Chebyshev polynomial kernel function for support rector regression machine [C]∥Proc of International Conference of Automatic Control and Artificial Intelligence. Herts SG12AY, UK: The Institution of Engineering and Technology, 2012:2364-2368.
YE N, SUN R, LIU Y, et al. Support vector machine with orthogonal Chebyshev kernel [C]∥Proc. of the 18th International Conference on Pattern Recognition. Los Alamitos, CA, USA: IEEE Computer Society, 2006:752-755.
OZER S, CHEN C H. Generalized Chebyshev kernels for support vector classification [C]∥Proc of the 19th International Conference on Pattern Recognition. Los Alamitos, CA, USA: IEEE Computer Society, 2008:1-4.
OZER S. On the classification performance of support vector machines using Chebyshev kernel functions [D]. Dartmouth, MA, USA: University of Massachusetts, 2007.
OZER S, CHEN C H, CIRPAN H A. A set of new Chebyshev kernel functions for support vector machine pattern classification [J]. Pattern Recognition, 2011, 44(7):1435-1447.
LANCKRIET G, BIE T D, CRISTIANINI N, et al. A statistical framework for genomic data fusion[J]. Bioinformatics, 2004, 20(16):2626-2635.
ZHANG L, ZHOU W, JIAO L. Wavelet support vector machine [J]. IEEE Trans Systems, Man, and Cybernetics: Part B Cybernetics, 2004, 34(1): 34-39.
ASUNCION A, NEWMAN D J. UCI machine learning repository [R/OL]. [2011-05-11]. http:∥www. ics.uci.edu/_mlearn/MLRepository.htmls, 2007.
0
浏览量
4
下载量
1
CSCD
关联资源
相关文章
相关作者
相关机构
京公网安备11010802024621