Large-Scale Data Classification Based on Ball Vector Machine
2013 ◽
Vol 312
◽
pp. 771-776
Keyword(s):
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.
1989 ◽
Vol 21
(1)
◽
pp. 99-114
◽
2019 ◽
Vol 74
(1)
◽
pp. 195-223
◽
Keyword(s):
2018 ◽
Vol 27
(2)
◽
pp. 1-15
◽
2015 ◽
Vol 5
(5)
◽
pp. 571-582
2017 ◽
Vol 159
(2)
◽
pp. 28-33