深哈伯量子回归网络的量子回归网络
Hristos Tyralis1, Georgia Papacharalampous2, Nilay Dogulu3
1Department of Topography, School of Rural, Surveying and Geoinformatics Engineering, National Technical University of Athens, Iroon Polytechniou 5, Zografou 157 80, Greece; Construction Agency, Hellenic Air Force, Mesogion Avenue 227-231, Cholargos 15 561, Greece.
概括
深度胡伯量子位回归网络 (DHQRN) 通过预测一般函数,嵌套量子位和预测量来提供先进的预测. 这种方法提高了深度学习应用中的不确定性量化.
科学领域:
- 机器学习 机器学习
- 深度学习 (Deep Learning) 是一种深度学习.
- 计量经济学 计量经济学
背景情况:
- 传统的机器学习回归侧重于使用平方或绝对误差的平均/中位数预测.
- 量子值和预测值对于量化预测不确定性至关重要,但标准深度学习模型存在局限性.
- 现有的深度学习方法,如量子回归神经网络 (QRNN) 和指向回归神经网络 (ERNN),针对特定的功能预测.
研究的目的:
- 介绍深度哈伯量子回归网络 (DHQRN) 作为一种新的深度学习方法.
- 开发一种方法,在一个单一的框架内概括量子和指数回归.
- 证明DHQRN在预测任务中的有效性,包括不确定性量化.
主要方法:
- 通过训练深度学习算法使用Huber量子数得分函数来开发DHQRN.
- DHQRN的设计是为了预测休伯定数,一个更一般的函数,包括定数和指数.
- 应用DHQRN来预测墨尔本和波士顿的房价,使用各种深度学习架构.
主要成果:
- DHQRN成功地预测了休伯定量体,将定量体和指量体结合在一起.
- 在真实世界住房数据集上评估了三种深度学习架构的预测性能.
- 经济案例研究和模拟实验证实了DHQRN的绝对性能令人满意.
结论:
- DHQRN为预测比现有方法更广泛的分布函数提供了一个统一的框架.
- 胡贝尔量子位计分函数与胡贝尔量子位计分函数一致,使强大的训练成为可能.
- DHQRN在现实应用中表现出强的性能,在深度学习模型中提供了改进的不确定性量化.
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