可靠的预测间隔与直接优化的诱导性合规回归用于深度学习
Haocheng Lei1, Anthony Bellotti1
1School of Computer Science, University of Nottingham Ningbo China, 199 Taikang East Road, Ningbo, 315100, Zhejiang, China.
概括
本研究引入了直接优化的诱导形式回归 (DOICR),以在深度学习中创建更窄的预测间隔 (PI). DOICR有效地控制了预测不确定性,同时确保了回归任务的准确数据覆盖.
科学领域:
- 机器学习 机器学习
- 人工智能的人工智能
- 统计 统计 统计 统计
背景情况:
- 深度学习回归模型需要准确的预测间隔 (PI) 来量化不确定性.
- 现有的方法往往减少PI宽度,但不能保证实际数据的覆盖.
- 归纳型合规预测器 (ICP) 可以保证覆盖范围,但没有针对最小PI宽度进行优化.
研究的目的:
- 在深度学习回归中开发一种新的方法来产生高质量的PI.
- 为了优化PI的狭窄宽度和保证数据覆盖.
- 提高深度学习预测的可靠性和精度.
主要方法:
- 为神经网络提出直接优化感应形式回归 (DOICR).
- 在优化过程中使用平均PI宽度作为唯一的损失函数.
- 确保PI通过捕获预设比例的真实标签来保持理论有效性.
主要成果:
- DOICR有效地将PI宽度降至最低,同时保持覆盖率保证.
- 拟议的方法的性能优于现有的最先进的算法.
- 在表格和图像数据集上,DOICR表现出卓越的性能.
结论:
- DOICR提高了深度学习回归中预测间隔的质量.
- 该方法提供了一个强大的方法,用于AI预测中的不确定性量化.
- 对于可靠的深度学习应用来说,DOICR是一个重大进步.
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