通过解决方案路径进行全球模型选择,用于稳固的支向量机器
IEEE transactions on pattern analysis and machine intelligence
|December 25, 2023
概括
强大的支持向量机器 (RSVM) 比传统的SVM提供了更好的概括性. 本研究介绍了SPRSVM,一种有效地找到最佳参数的方法,与网格搜索相比,大大降低了计算成本.
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:
- 强大的支持矢量机器 (RSVM) 具有坡道损失的性能优于使用链损失的传统支持矢量机器 (SVM).
- 有效的RSVM性能依赖于最佳的规范化和坡道参数.
- 对参数选择的网格搜索是计算密集的,特别是在细粒度搜索中.
研究的目的:
- 提出一种有效的方法来选择RSVM的最佳规范化和坡道参数.
- 为了解决与传统参数调技术相关的高计算成本.
主要方法:
- 介绍基于凸程序 (CCCP) 的RSVM (SPRSVM) 的解决方案路径,以跟踪非凸的RSVM解决方案.
- 使用增量和减量学习算法在解决方案路径跟踪过程中管理Karush-Khun-Tucker违反样本.
- 利用模型函数的断片线性来计算错误路径并确定最佳参数.
主要成果:
- 通过SPRSVM,可以在全球范围内搜索调节和坡道参数.
- 与网格搜索相比,显著减少了计算时间.
- 验证了SPRSVM的有限收,并分析了它的计算复杂性.
结论:
- SPRSVM提供了一种高效和有效的方法来调整RSVM参数.
- 该方法显著降低了计算负担,使RSVM在现实应用中更加实用.
- SPRSVM确保了最佳的参数选择,以改善模型通用化.
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