一类增量式支持向量机的分析

针对训练数据发生增量改变时,标准一类支持向量机的批处理算法需要重新进行训练,不适合在线增量环境学习的问题,提出一种详细的增量式标准一类分类向量机算法,并通过理论分析对该算法的可行性和有限收敛性进行了证明,确保该算法的每步调整都是可靠的,并确保该算法通过有限步调整最终收敛到问题的最优解。在标准数据集上的实验结果验证了理论分析的正确性。

Analysis of One Class Incremental Support Vector Machine

Batch implementations of standard one class support vector machine (SVM) are inefficient on an on-line setting because they must be retrained from scratch every time when the training set is incremental modified. To solve this problem, a detailed incremental one class SVM algorithm is given, and the feasibility and the finite convergence of the algorithm are proven through theoretical analysis. It is ensured that each adjustment step in the C&P algorithm is reliable, and the algorithm will converge to the optimal solution within finite steps. The experimental results on benchmark datasets verify the correctness of theoretical analysis.