模式识别是一个成熟的、令人兴奋的、快速发展的领域,它支撑着计算机视觉、图像处理、文本和文档分析以及神经网络等相关领域的发展。它与机器学习非常相似,在生物识别、生物信息学、多媒体数据分析和最新的数据科学等新兴领域也有应用。模式识别(Pattern Recognition)杂志成立于大约50年前,当时该领域刚刚出现计算机科学的早期。在这期间,它已大大扩大。只要这些论文的背景得到了清晰的解释并以模式识别文献为基础,该杂志接受那些对模式识别理论、方法和在任何领域的应用做出原创贡献的论文。 官网地址:http://dblp.uni-trier.de/db/conf/par/

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In industrial manufacturing, modern high-tech equipment delivers an increasing volume of data, which exceeds the capacities of human observers. Complex data formats like images make the detection of critical events difficult and require pattern recognition, which is beyond the scope of state-of-the-art process monitoring systems. Approaches that bridge the gap between conventional statistical tools and novel machine learning (ML) algorithms are required, but insufficiently studied. We propose a novel framework for ML based indicators combining both concepts by two components: pattern type and intensity. Conventional tools implement the intensity component, while the pattern type accounts for error modes and tailors the indicator to the production environment. In a case-study from semiconductor industry, our framework goes beyond conventional process control and achieves high quality experimental results. Thus, the suggested concept contributes to the integration of ML in real-world process monitoring problems and paves the way to automated decision support in manufacturing.

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In industrial manufacturing, modern high-tech equipment delivers an increasing volume of data, which exceeds the capacities of human observers. Complex data formats like images make the detection of critical events difficult and require pattern recognition, which is beyond the scope of state-of-the-art process monitoring systems. Approaches that bridge the gap between conventional statistical tools and novel machine learning (ML) algorithms are required, but insufficiently studied. We propose a novel framework for ML based indicators combining both concepts by two components: pattern type and intensity. Conventional tools implement the intensity component, while the pattern type accounts for error modes and tailors the indicator to the production environment. In a case-study from semiconductor industry, our framework goes beyond conventional process control and achieves high quality experimental results. Thus, the suggested concept contributes to the integration of ML in real-world process monitoring problems and paves the way to automated decision support in manufacturing.

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