贝叶斯对多样性增强主动学习的平均正确分数的估计
IEEE transactions on pattern analysis and machine intelligence
|December 14, 2023
概括
我们介绍了贝叶斯平均正确分数估计 (BEMPS) 对于高效的积极学习 (AL). 最好的环境管理系统 (BEMPS) 提高了分类器的性能和对文本和图像数据的校准.
科学领域:
- 机器学习 机器学习
- 计算机视觉 计算机视觉
- 自然语言处理自然语言处理.
背景情况:
- 积极学习 (AL) 的有效性取决于获取功能的抽样效率.
- 预期损失减少 (ELR) 估计贝叶斯减少了分类错误.
- 一般成本可以纳入ELR框架.
研究的目的:
- 提出贝叶斯平均正确分数估计法 (BEMPS) 来估计严格正确分数的增加.
- 开发一个批量AL算法,促进预期得分变化的多样性.
- 结合深层组合,动态验证集和蒙特卡罗脱落来实现高性能分类器.
主要方法:
- 实施了BEMPS,用于在ELR框架内估计得分增加.
- 开发了一个批量AL算法,鼓励多样性.
- 使用具有动态验证和蒙特卡洛脱落的深度合集来增强分类器.
- 对文本和图像数据集进行了广泛的实验.
主要成果:
- 具有平均平方误差和日志概率的BEMPS证明了强大的采集功能.
- 由BEMPS制造的分类器被精心校准,并且表现优于现有方法.
- 使用数据图和t-SNE图的定性分析支持了BEMPS的优势.
结论:
- BEMPS提供了一种强大而通用的方法,用于积极学习的功能.
- 提出的方法可以提高分类器的性能和校准.
- 在积极学习中,BEMPS为现有的获取功能提供了强有力的替代方案.
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