一个简单可靠的实例选择快速培训支持矢量机器:有效的边界识别
Long Tang1, Yingjie Tian2, Xiaowei Wang3
1School of Artificial Intelligence, Nanjing University of Information Science & Technology, Nanjing, 210044, China; Research Institute of Talent Big Data, Nanjing University of Information Science & Technology, Nanjing, 210044, China.
概括
新的实例选择 (IS) 方法,有效边界识别 (VBR) 和加强的VBR (SVBR),有效地减少了大型数据集上的支持向量机 (SVM) 的训练时间,同时保持了准确性.
科学领域:
- 机器学习 机器学习
- 计算统计学 计算统计学
背景情况:
- 支持向量机器 (SVM) 面临的训练复杂性挑战与大型数据集.
- 现有的实例选择 (IS) 方法难以平衡准确性和计算效率.
研究的目的:
- 开发新的实例选择方法,以提高SVM培训效率.
- 解决当前IS技术在处理大规模数据方面的局限性.
主要方法:
- 引入有效边界识别 (VBR) 以根据异质邻居来选择关键实例.
- 开发了一个强化版本 (SVBR),改进了实例选择以提高可靠性.
- 将IS纳入高斯核子矩阵缩小,以尽量减少执行时间.
主要成果:
- VBR和SVBR在减少培训和推断时间方面表现出有效性.
- 与现有方法相比,拟议的方法保持或提高了分类准确性.
- 在基准和合成数据集上的实验验证证证了VBR和SVBR的有效性.
结论:
- 在大型数据集上,VBR和SVBR为高效的SVM培训提供了可行的解决方案.
- 提出的方法在实例选择中成功平衡了准确性和计算效率.
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