基于半监督的跨域媒介区分分析和内核转移的域名适应 极端机器学习
1College of Information Engineering, Henan University of Science and Technology, Kaiyuan Avenue, Luoyang 471023, China.
Sensors (Basel, Switzerland)
|July 14, 2023
概括
本研究引入了一个域适应方法,以改善在数据分布不同时的模式识别. 拟议的方法增强了特征提取和分类,以实现更强大的跨领域视觉分析.
科学领域:
- 计算机科学 计算机科学
- 机器学习 机器学习
- 模式识别 模式识别
背景情况:
- 传统的特征提取和分类模型在训练和测试数据分布不匹配时会退化.
- 域名转移是现实世界模式识别任务中的一个重大挑战.
研究的目的:
- 提出一种新的域调整方法,以应对域转移造成的性能退化.
- 提高跨不同数据分布的模式识别的稳定性和准确性.
主要方法:
- 在半监督区分分析 (SDA) 中引入跨域平均近似 (CDMA),以开发半监督跨域平均区分分析 (SCDMDA),用于共享特征提取.
- 使用内核极端学习机器 (KELM) 作为分类器,并通过结合跨域平均约束来改进知识传输,开发了内核转移极端学习机器 (KTELM).
主要成果:
- 拟议的SCDMDA和KTELM方法在与现有的最先进技术相比显示出更高的性能.
- 在四个现实世界的跨领域视觉数据集上的实验验验证了该方法的有效性.
结论:
- 开发的域调整策略有效地处理数据分布中的差异.
- 拟议的方法为跨领域的模式识别挑战提供了具有竞争力和强大的解决方案.
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