高级检索

    球向量机的快速在线学习

    Efficiently Training Ball Vector Machine in Online Way

    • 摘要: 在大数据分析处理中,有效学习样本逐渐增加,据此如何高效渐进地学习分类器是一个非常有价值的问题.相比于支撑向量机和核向量机,球向量机自身在批量样本学习中具有速度快、准确率高的特点,但该方法不适合快速的在线学习.针对该问题提出了在线球向量机.首先将二分类问题转为两个单分类问题,利用球向量机(ball vector machine, BVM)对超球球心的更新算法对每一个训练向量仅迭代一次,求得两个高维超球的球心,随后直接利用两个高维超球球心的垂直平分面进行分类.理论分析证明了新方法的有效性,与现有在线增量学习方法的实验比较结果表明,在线球向量机(online ball vector machine, OBVM)在时间计算复杂度和综合性能方面有显著优势.

       

      Abstract: In many real classification problems, labeled samples may be received step by step. Correspondingly, how to efficiently and progressively train classifiers should be seriously concerned. Ball vector machine (BVM), compared with support vector machine and core vector machine, can be more efficiently trained with approximate performance. In terms of the intrinsic features of BVM, an online BVM (OBVM) is presented in this paper, whose effectiveness is analytically trustable. OBVM transforms a binary classification problem into two single classification problems, in which every class is modeled by a hyper-center. Two hyper-centers are incrementally updated by using the same strategy presented in BVM. Moreover, the perpendicular bisector of plane of two hyper-centers is used to classify data. The experimental results on several standard classification datasets and a practical application indicate that, OBVM has remarkable advantages on the time complexity of training one samples per time, and also can gain high classification accuracy. Therefore, OBVM provides a new generally effective solution to train classifiers with online way and could be applied to many practical problems.

       

    /

    返回文章
    返回