JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE) ›› 2015, Vol. 45 ›› Issue (1): 13-18.doi: 10.6040/j.issn.1672-3961.1.2014.072

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An algorithm of fast local support vector machine based on clustering

HAO Qingbo1, MU Shaomin1,2, YIN Chuanhuan3, CHANG Tengteng1, CUI Wenbin1   

  1. 1. School of Information Science and Engineering, Shandong Agricultural University, Taian 271018, Shandong, China;
    2. Agricultural Big-Data Research Center, Shandong Agricultural University, Taian 271018, Shandong, China;
    3. School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China
  • Received:2014-03-26 Revised:2015-01-08 Online:2015-02-20 Published:2014-03-26

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Abstract: In order to further improve the classification efficiency and precision of local support vector machine, a new algorithm was proposed.The two major improvements were as follows. First, every type of training samples was clustered seperately, and the training samples were substituted for sample centers generated by clustering. Second, the k nearest neighbors of test samples were selected by using the improved k-nearest neighbor algorithm. Tests were done on UCI data sets and bark image data sets made by the proposed algorithm to verify its effectiveness. Experimental results demonstrated that this algorithm had certain superiority of classification accuracy and efficiency.

Key words: kernel function, local support vector machine, texture features, k-nearest neighbor, k-means clustering, classification

CLC Number: 

  • TP391
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