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快速的支持结构核向量机

Fast Support Vector Machines for Structural Kernels
课程网址: http://videolectures.net/ecmlpkdd2011_severyn_fast/  
主讲教师: Aliaksei Severyn
开课单位: 特伦托大学
开课时间: 2011-10-03
课程语种: 英语
中文简介:
在本文中,我们提出了近似切割平面算法(CPA)的三个重要增强,以训练具有结构核的支持向量机:(i)我们利用有向无环图来利用切割平面模型的紧凑而精确的表示来加速训练和分类,(ii)我们提供并行实现,使训练量与CPU的数量几乎呈线性关系,以及(iii)我们提出了一种替代的采样策略来处理类不平衡问题,并表明理论上的收敛边界得以保留。对三个不同数据集的实验评估证明了我们的方法的可靠性以及使用结构核进行快速学习和分类的可能性。
课程简介: In this paper, we propose three important enhancements of the approximate cutting plane algorithm (CPA) to train Support Vector Machines with structural kernels: (i) we exploit a compact yet exact representation of cutting plane models using directed acyclic graphs to speed up both training and classification, (ii) we provide a parallel implementation, which makes the training scale almost linearly with the number of CPUs, and (iii) we propose an alternative sampling strategy to handle class-imbalanced problem and show that theoretical convergence bounds are preserved. The experimental evaluations on three diverse datasets demonstrate the soundness of our approach and the possibility to carry out fast learning and classification with structural kernels.
关 键 词: 切削平面算法; 支持向量机; 抽样策略
课程来源: 视频讲座网
最后编审: 2020-06-24:yumf
阅读次数: 63