对依赖实例的标签噪声的一个参数模型
概括
本研究引入了一种新方法,通过建模从贝叶斯最佳标签到噪声标签的过渡来估计依赖实例的标签噪声过渡矩阵. 这种方法可以提高分类器在标签噪音学习场景中的性能.
科学领域:
- 机器学习 机器学习
- 计算机科学 计算机科学
背景情况:
- 估计过渡矩阵对于标签噪音学习中的统计学上一致的分类器至关重要.
- 现有的方法,如清洁标签过渡矩阵 (CLTM),与依赖实例的标签噪声作斗争.
研究的目的:
- 提出一种用于估计依赖实例的标签噪声过渡矩阵的新方法.
- 通过建模从贝叶斯最佳标签到噪音标签 (BLTM) 的过渡来预测贝叶斯最佳标签的分类器.
主要方法:
- 直接建模贝叶斯-标签过渡矩阵 (BLTM) 从贝叶斯最佳标签到噪音标签.
- 利用贝叶斯最佳标签 (一热向量) 的固有确定性来识别可靠的培训示例.
- 使用深度神经网络来参数化和估计依赖实例的过渡矩阵.
主要成果:
- 建议的BLTM方法有效地处理依赖实例的标签噪声,优于传统的CLTM方法.
- 该方法允许从噪音数据中收集理论上保证的贝叶斯最佳标签.
- 深度神经网络能够准确估计依赖实例的过渡矩阵,从而提高了概括性.
结论:
- 模拟BLTM比CLTM提供了显著的优势,例如依赖标签噪声.
- 提出的基于深度学习的方法提高了噪音标签学习中的分类性能和概括性.
- 这项工作为处理机器学习中的复杂标签噪声模式提供了更强大的框架.
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