Large-Scale Data Classification Based on Ball Vector Machine

• Published in: Applied Mechanics and Materials Vol. 312; pp. 771 - 776
• Main Authors: Zhao, Fei, Cheng, Guo Jian, Zheng, Min Juan
• Format: Journal Article
• Language: English
• Published: Zurich Trans Tech Publications Ltd 01.02.2013
• Subjects: Enclosed Ball; Core Vector Machine; Minimum Enclosed Ball; Support Vector Machine (SVM); Ball Vector Machine
• ISBN: 3037856904; 9783037856901
• ISSN: 1660-9336; 1662-7482; 1662-7482
• DOI: 10.4028/www.scientific.net/AMM.312.771

The quadratic programming problem in the standard support vector machine (SVM) algorithm has high time complexity and space complexity in solving the large-scale problems which becomes a bottleneck in the SVM applications. Ball Vector Machine (BVM) converts the quadratic programming problem of the traditional SVM into the minimum enclosed ball problem (MEB). It can indirectly get the solution of quadratic programming through solving the MEB problem which significantly reduces the time complexity and space complexity. The experiments show that when handling five large-scale and high-dimensional data sets, the BVM and standard SVM have a considerable accuracy, but the BVM has higher speed and less requirement space than standard SVM.