|Table of Contents|

Incremental Vector Support Vector Machine Learning Algorithm

《南京理工大学学报》(自然科学版)[ISSN:1005-9830/CN:32-1397/N]

Issue:
2012年05期
Page:
873-
Research Field:
Publishing date:

Info

Title:
Incremental Vector Support Vector Machine Learning Algorithm
Author(s):
CHEN Yuan-tao12XU Wei-hong12WU Jia-ying12
1. School of Computer Science and Technology,NUST,Nanjing 210094,China; 2. School of Computer and Communicational Engineering,Changsha University of Science and Technology, Changsha 410114,China
Keywords:
support vector machineincrement vectorpruningsome clips
PACS:
TP391
DOI:
-
Abstract:
In view of that the common support vector machine( SVM) learning algorithm is time- consuming and lower in efficiency,an algorithm of incremental vector support vector machine( IV- SVM) learning algorithm is put forward here. A primary SVM classifier is acquired based on the selected increment vectors in the kernel space. According to Karush-Kuhn-Tucker(KKT)conditions, the original training samples are pruned through the primary SVM classifier. The final SVM classifier is obtained by training the primary SVM classifier with reduction samples. Simulation experiments show that,compared with the common support vector machine,IV-SVM can reduce the training time of large capacity data samples support vector machine.

References:

[1]Platt J C,Scholkopf B,Burges C J C,et al. Fast training of SVMs using sequential minimal optimization [ A]. Advances in Kernel Methods Support Vector Learning [C]. Massachusetts,US:MIT Press,1998:185-208.
[2] Keerthi S, Shevade S, Bhattacharyya C, et al. Improvements to platt爷s SMO algorithm for SVM classifier design[ J]. Neural Networks,1999,6(12): 783-789.
[3] Zhang Ling,Zhang Bo. A geometrical representation of McCulloch-pitts neural model and its applications[J]. IEEE Transactions on Neural Networks,1999,10(4): 925-929.
[4] 孙林,杨世元. 基于LS-SVM 的温度传感器非线性关 系拟合及参考端温度补偿[ J]. 应用科学学报,27 (6):616-622. Sun Lin,Yang Shiyuan. Fitting of non-linear relation of temperature sensor and reference temperature compensation based on LS-SVM [ J ]. Journal of Applied Sciences,2009,27(6):616-622.
[5] Zhang Li, Zhou Weida, Jiao Licheng. Pre-extracting support vectors for support vector machine[A]. WCCC- ICSP Proceedings of 5th International Conference on Signal Processing [ C ]. Beijing: Publishing House Electronics Industry,2000:1427-1431.
[6] 姜慧妍,宗茂,刘相莹. 基于ACO-SVM 的软件缺陷 预测模型的研究[ J]. 计算机学报,2011,34(6): 1148-1154. Jiang Huiyan, Zong Mao, Liu Xiangying. Research of software defect prediction model based on ACO-SVM [J]. Chinese Journal of Computers,2011,34(6):1148 -1154.
[7] 李红莲,王春花,袁保宗,等. 针对大规模训练集的 支持向量机的学习策略[ J]. 计算机学报,2004,27 (5):715-719. Li Honglian, Wang Chunhua, Yuan Baozong, et al. A learning strategy of SVM used to large training set[J]. Chinese Journal of Computers,2004,27(5):715-719.
[8] Canu S,Grandvalet Y,Guigue V,et al. SVM and kernel methods Matlab toolbox[ EB/ OL]. http:/ / asi. insa - rouen. fr/ ~ arakotom/ toolbox/ ,2005-12-20.
[9] 张培林,钱林方,曹建军,等. 基于蚁群算法的支持 向量机参数优化[ J]. 南京理工大学学报,2009,33 (4):464-468. Zhang Peilin, Qian Linfang, Cao Jianjun, et al. Parameter optimization of support vector machine based on ant colony optimization algorithm [ J]. Journal of Nanjing University of Science and Technology,2009, 33(4):464-468.
[10] 邢永忠,吴晓蓓,徐志良. 基于矢量基学习的自适应 迭代最小二乘支持向量机回归算法[ J]. 南京理工 大学学报,2011,35(3):328-333. Xing Yongzhong, Wu Xiaobei, Xu Zhiliang. Adaptive iterative LS-SVM regression algorithm based on vector base learning [ J]. Journal of Nanjing University of Science and Technology,2011,35(3):328-333.

Memo

Memo:
-
Last Update: 2012-11-26