模糊语言的多衍生解析 - - 语法模式识别的新范式
IEEE transactions on pattern analysis and machine intelligence
|February 19, 2024
概括
语法模式识别 (SPR) 现在可以使用多衍生解析和模糊原始来处理模糊模式. 这种方法可以提高模式识别准确度,用于诸如负载预测和医学图像分析等应用.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 模式识别 模式识别
背景情况:
- 在模式识别方面,识别模糊或扭曲的模式仍然是一个重大挑战.
- 现有的方法在与固有的模糊性和现实数据中的噪音作斗争.
研究的目的:
- 引入一种新的语法模式识别 (SPR) 范式,能够处理模糊的语言.
- 为模糊/扭曲的模式开发一个高效的解析方法.
- 介绍SPR系统的通用架构,并展示其应用.
主要方法:
- 引入了由模糊原始体定义的模糊语言概念 (带有距离,概率,模糊度量度的矢量).
- 开发了一种基于动态编程语法的高效多衍生解析方法.
- 利用模糊的原始词来识别最佳的结构模板,以提高识别.
主要成果:
- 拟议的SPR方法有效地解决了模糊/扭曲模式的识别问题.
- 在短期电力负载预测中证明了成功的应用.
- 展示了分析超声波图像用于诊断胎儿宫缺陷的实用性.
结论:
- 新的SPR范式为含有模糊或不准确数据的模式识别提供了强大的解决方案.
- 开发的方法和系统架构是多功能的,适用于不同的领域.
- 实验结果验证了拟议方法的有效性和充分性.
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