一个可控制的帕雷托前线学习框架,具有完成的缩放函数及其应用.
Tran Anh Tuan1, Long P Hoang2, Dung D Le2
1School of Applied Mathematics and Informatics, Hanoi University of Science and Technology, Ha Noi, Viet Nam.
概括
本研究介绍了用于多目标优化 (MOO) 的可控帕雷托前线学习 (CPFL). 与现有方法相比,CPFL准确地接近帕雷托前线,计算效率得到了提高.
科学领域:
- 多目标优化 (MOO) 是指多目标优化.
- 计算智能是一种计算智能.
- 机器学习 机器学习
背景情况:
- 巴雷托前线学习 (PFL) 在MOO中接近巴雷托前线.
- 之前的PFL方法缺乏偏好向量和帕雷托最佳解决方案之间的清晰映射.
研究的目的:
- 开发一个全面的PFL框架,称为可控制的帕雷托前线学习 (CPFL).
- 通过将伪凸标量化与超级网络相结合,解决以前PFL方法中的模两可.
主要方法:
- 用伪凸的标量化函数证明了MOO的收和完成.
- 将这些方面整合到CPFL的超级网络框架中.
主要成果:
- 拟议的CPFL框架在接近帕雷托前线方面实现了高精度.
- 与以前的方法相比,CPFL在推断过程中显著降低了计算成本.
结论:
- CPFL提供了一种强大而高效的多目标优化方法.
- 该框架提供了一种全面的解决方案,用于学习可控方面的帕雷托前线.
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