过错误标记的训练实例使用黑子优化和量子化
Makoto Otsuka1,2, Kento Kodama3, Keisuke Morita3,4
1LiLz Inc., Okinawa, Japan. m.otsuka@lilz.jp.
Scientific reports
|October 30, 2025
概括
这项研究引入了一种新的方法,通过使用黑子优化和量子回火来清除错误标记的实例来清除杂的训练数据. 这种方法通过提高数据集质量来增强机器学习模型的概括性.
科学领域:
- 机器学习 机器学习
- 数据科学数据科学数据科学
- 量子计算是一种量子计算.
背景情况:
- 在训练数据集中错误标记的实例会降低模型的概括性.
- 有效的消除噪音策略对于现实应用至关重要.
- 现有的方法可能缺乏可扩展性或处理噪音数据的效率.
研究的目的:
- 提出一种强大而有效的方法,从受污染的培训数据集中删除错误标记的实例.
- 通过提高培训数据质量来提高机器学习模型的概括能力.
- 为了利用量子化来有效地采样高质量的培训子集.
主要方法:
- 结合了基于替代模型的黑子优化 (BBO) 和后期处理.
- 使用量子回火来有效地采样各种训练子集,并具有较低的验证误差.
- 评估基于验证损失的过训练子集,并代地改进损失估计.
主要成果:
- 拟议的方法有效地优先删除高风险错误标记的实例.
- 与D-Wave的物理量子化器集成,与模拟化相比,显示出更快的优化和更高质量的子集.
- 该方法提供了一个可扩展的框架,用于提高监督学习中的数据集质量.
结论:
- 开发的方法对于监督学习任务是有效的,提高了数据集质量和模型概括性.
- 量子化,特别是在物理硬件上,在优化速度和子集质量方面提供了优势.
- 未来的工作包括将该方法应用于无监督学习,现实世界数据集和大规模实施.
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