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A non-parametric learning algorithm for small manufacturing data sets

机译：用于小型制造数据集的非参数学习算法

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摘要

Nowadays the manufacturing environment changes promptly owing to globalization and innovation. It is noteworthy that the life cycle of products consequently becomes shorter and shorter. Although data mining techniques are widely employed by researchers to extract proper management information from the data, scarce data can only be obtained in the early stages of a manufacturing system. From the view of machine learning, the size of training data significantly influences the learning accuracies. Learning based on limited experience will be a tough task. On account of the cause, this research systematically estimates the data behavior such as the trend and potency to capture the dependency within a sequence of time series data. It should also be added that the analyzed data in this article are dependent examples that come from different populations. This research proposes a non-parametric learning algorithm instead of using parametric statistics for small-data-set learning. The proposed algorithm named the trend and potency tracking method (TPTM) attempts to explore the predictive information through the generation of trend and potency (TP) value of each datum. The extra information extracted from the data trend and potency proves that it can speed up stabilizing the learning task and can dynamically improve the derived knowledge from the occurrence of the latest data.
机译：如今，由于全球化和创新，制造环境迅速发生了变化。值得注意的是，产品的生命周期因此变得越来越短。尽管研究人员广泛地使用数据挖掘技术从数据中提取适当的管理信息，但是稀有数据只能在制造系统的早期阶段获得。从机器学习的角度来看，训练数据的大小会显着影响学习的准确性。根据有限的经验学习将是一项艰巨的任务。由于原因，本研究系统地估计了数据行为，例如趋势和潜力，以捕获时间序列数据序列中的依存关系。还应该补充的是，本文中的分析数据是来自不同人群的相关示例。这项研究提出了一种非参数学习算法，而不是将参数统计信息用于小数据集学习。提出的名为趋势和效能跟踪方法（TPTM）的算法尝试通过生成每个数据的趋势和效能（TP）值来探索预测信息。从数据趋势和潜能中提取的额外信息证明，它可以加快稳定学习任务的速度，并可以根据最新数据的出现而动态地改进派生的知识。

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