有效的多任务学习,具有适应性的时间结构,用于进展预测
Menghui Zhou1, Yu Zhang2, Tong Liu2
1Department of Software, Yunnan University, Kunming, 674199 Yunnan Province China.
Neural computing & applications
|June 26, 2023
概括
本研究介绍了一种有效的多任务学习方法,用于时间变化的进展问题. 它具有适应性全球时间关系结构 (AGTS),以提高性能和效率.
科学领域:
- 机器学习 机器学习
- 数据科学数据科学数据科学
- 时间序列分析时间序列分析
背景情况:
- 现有的多任务学习 (MTL) 方法因特征选择或任务关系优化方面的局限性而面临进展问题.
- 当前的方法往往无法捕捉复杂的任务间关系,或遭受高计算复杂性.
研究的目的:
- 为不断变化的状态的进展问题提出一种新且高效的多任务学习公式.
- 开发一种有效利用跨任务共享知识的方法,同时解决现有MTL技术的局限性.
主要方法:
- 引入了适应性全球时间关系结构 (AGTS) 来建模时间点之间的关系.
- 集成稀少组拉索和与AGTS合并的拉索形成凸起的MTL配方.
- 开发了高效的优化算法,使用乘数交替方向方法 (ADMM) 和加速梯度方法.
主要成果:
- 拟议的配方执行有效的特征选择,并捕获全球时间任务相关性.
- 优化算法有效地处理与配方固有的非平滑惩罚.
- 在四个现实世界数据集上的实验结果显示,与基线MTL方法相比,其效率和效率更高.
结论:
- 新的凸 MTL 配合 AGTS 的配方显著提高了对进展问题的性能.
- 开发的优化策略确保了计算效率,使该方法适用于现实世界的应用.
- 这种方法为分析动态系统和时间变化的数据提供了强大而高效的解决方案.
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