|
计算机应用研究 2007
SMO algorithm based on reserve working set strategy
|
Abstract:
In order to improve the training speed for large-scale problem, proposed a new strategy for the working set selection in SMO algorithm based on the tradeoff between the cost on working set selection and cache performance. This new strategy selected several maximal violating samples from cache as the reserve working set which would provide iterative working sets for the next several optimizing steps. The new strategy could improve the efficiency of the kernel cache and reduce the computational cost related to the working set selection. The results of theories and experiments demonstrate that the proposed method can reduce the training time, especially for large datasets.