对于深度学习分类器的学习顺序-层次约束
IEEE transactions on neural networks and learning systems
|February 13, 2024
概括
本研究引入了新的深度学习 (DL) 模型,层次累积链接模型 (HCLM) 和层次-顺序二进制分解 (HOBD),以解决具有层次和顺序结构的复杂分类任务,提高概括性能.
科学领域:
- 机器学习 机器学习
- 人工智能的人工智能
- 数据科学数据科学数据科学
背景情况:
- 现实世界的分类通常涉及具有固有的层次和顺序关系的类别.
- 现有的深度学习 (DL) 模型难以同时捕捉这些层次和顺序约束,限制了概括性能.
研究的目的:
- 提出新的DL方法,有效地模拟分类中的层次结构和顺序结构.
- 通过整合这些约束来提高复杂分类问题的概括性能.
主要方法:
- 引入了两个新的顺序层次DL方法:层次累积链接模型 (HCLM) 和层次-顺序二进制分解 (HOBD).
- 将问题分解为本地和全球图路径,以编码每个层次层级的顺序约束.
- 设置了这个问题,将全球和本地损失的组合最小化,使用顺序二进制分解 (OBD) 和累积链接模型 (CLM) 进行约束设置.
主要成果:
- 拟议的HCLM和HOBD模型在统计学上显示出比最先进的方法有显著的改进.
- 在工业,生物医学,计算机视觉和金融领域的各种现实数据集中验证了有效性.
- 这些模型成功地捕获并利用了分层和顺序标签结构.
结论:
- 新的顺序层次DL方法为具有复杂标签结构的分类任务提供了显著的进步.
- 这些方法提供了一个强大的框架,可以在各种科学和工业领域改善对现实应用的概括性.
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