对于线性稀疏 SVM 的近接梯度方法的线性收
Xiaoqi Jiao1, Heng Lian2, Jiamin Liu3
1Academy of Statistics and Interdisciplinary Sciences KLATASDS-MOE, East China Normal University, Shanghai, China; Department of Decision Analytics and Operations, City University of Hong Kong, Hong Kong, China.
这项研究使用近接梯度方法证明了稀疏线性支向量机 (SVM) 的线性收,即使在没有强凸链损失的情况下也能达到统计准确度. 这些发现突出了有效的趋同到人口真相.
科学领域:
- 机器学习 机器学习
- 优化算法 优化算法
- 统计学学习理论
背景情况:
- 支持向量机 (SVM) 是一个强大的分类工具.
- 链损失通常用于SVM,但缺乏强大的凸度/光滑性质.
- 对于非强烈凸起的问题来说,实现线性收率是具有挑战性的.
研究的目的:
- 为了确定与链损失的稀疏线性SVM的线性收率.
- 分析这个问题的近接梯度方法的性能.
- 调查数字和统计趋同之间的相互作用.
主要方法:
- 在复合函数中使用近位梯度法.
- 将该方法应用于一系列规范化参数,以计算近似的解决方案路径.
- 分析了考虑到数值和统计方面的趋同率.
主要成果:
- 对于稀疏线性SVM的确定的线性收率,直至统计准确度.
- 证明了对人口真相的趋同,不一定是确切的解决方案.
- 表明O(log*s*) 代足以获得近似的解决方案,而O(log n) 阶段几乎是预言速率.
结论:
- 靠近梯度方法可以实现对有链损失的稀疏线性SVM的线性收.
- 该方法有效地平衡了数值和统计趋同.
- 这些发现为稀疏的SVM优化提供了理论上的保证.
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