计算机视觉应用中基于规则推理的基于计算智能的机器学习方法

T. Dhivyaprabha, P. Subashini, M. Krishnaveni
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引用次数: 15

摘要

在机器人控制中,用于理解数据的规则发现是至关重要的。基本上,对数据的理解取决于逻辑规则、相似性评估和图形方法。专家系统利用机器学习技术,通过探索匿名环境,单独收集训练样例。在动态环境中,未来的行动是由感知序列决定的,因此编码为规则库。本文的重点是展示图像理解逻辑规则的提取和应用,使用新开发的协同成纤维细胞优化(SFO)算法和已知的现有人工学习方法。SFO算法在密歇根方法和匹兹堡方法两种模式下进行了测试。通过对连续数据的描述,验证优化阶段的精度和误差水平,对最优规则发现进行评价。在这项工作中,Monk的问题是通过发现最优规则来解决的,这些规则增强了机器人分类系统从提取的属性中对物体进行分类的泛化和可理解性,从而有效地对其领域进行分类。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Computational intelligence based machine learning methods for rule-based reasoning in computer vision applications
In robot control, rule discovery for understanding of data is of critical importance. Basically, understanding of data depends upon logical rules, similarity evaluation and graphical methods. The expert system collects training examples separately by exploring an anonymous environment by using machine learning techniques. In dynamic environments, future actions are determined by sequences of perceptions thus encoded as rule base. This paper is focused on demonstrating the extraction and application of logical rules for image understanding, using newly developed Synergistic Fibroblast Optimization (SFO) algorithm with well-known existing artificial learning methods. The SFO algorithm is tested in two modes: Michigan and Pittsburgh approach. Optimal rule discovery is evaluated by describing continuous data and verifying accuracy and error level at optimization phase. In this work, Monk's problem is solved by discovering optimal rules that enhance the generalization and comprehensibility of a robot classification system in classifying the objects from extracted attributes to effectively categorize its domain.
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