图表数据的表示学习:一个全面的调查调查
IEEE transactions on pattern analysis and machine intelligence
|January 30, 2026
概括
这项调查探讨了表式表示学习,重点是用于分类和回归的深度神经网络 (DNN). 它通过概括对模型进行分类,并讨论表式机器学习的进步.
科学领域:
- 机器学习 机器学习
- 数据科学数据科学数据科学
- 人工智能的人工智能
背景情况:
- 表格式数据在机器学习中普遍用于分类和回归.
- 深度神经网络 (DNN) 通过表示学习在表格数据中显示出前景.
研究的目的:
- 系统地调查表式表示学习领域.
- 涵盖背景,挑战,基准,以及DNN的优缺点.
- 根据概括能力组织现有方法.
主要方法:
- 模型的分类为专业,可转让和一般.
- 专业模型 (特征,样本,目标) 的层次分类.
- 探索特征和样本表示的策略.
主要成果:
- 在专业模型中提供高质量表示的详细策略.
- 可转移模型的预训练和微调方法.
- 跨数据集的一般表式基础模型的适应策略.
结论:
- 讨论集合方法和扩展,如开放环境学习和多式模式学习.
- 对表式理解任务的概述.
- 为表格表示学习研究提供了全面的资源.
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