Machine Learning requires a large amount of training data in order to build accurate models. Sometimes the data arrives over time, requiring significant storage space and recalculating the model to account for the new data. On-line learning addresses these issues by incrementally modifying the model as data is encountered, and then discarding the data. In this study we introduce a new online linear regression approach. Our approach combines newly arriving data with a previously existing model to create a new model. The introduced model, named OLR-WA (OnLine Regression with Weighted Average) uses user-defined weights to provide flexibility in the face of changing data to bias the results in favor of old or new data. We have conducted 2-D and 3-D experiments comparing OLR-WA to a static model using the entire data set. The results show that for consistent data, OLR-WA and the static batch model perform similarly and for varying data, the user can set the OLR-WA to adapt more quickly or to resist change.

翻译：机器学习需要大量训练数据来构建精确模型。有时数据随时间逐步到达，需要大量存储空间并重新计算模型以纳入新数据。在线学习通过遇到数据时增量调整模型并丢弃原始数据来解决这些问题。本研究提出了一种新的在线线性回归方法，该方法将新到达的数据与先前存在的模型相结合以创建新模型。所提出的模型名为OLR-WA（Online Regression with Weighted Average，加权平均在线回归），通过用户自定义权重在应对数据变化时提供灵活性，使结果偏向旧数据或新数据。我们进行了二维和三维实验，将OLR-WA与使用完整数据集的静态模型进行比较。结果表明：对于稳定数据，OLR-WA与静态批处理模型表现相似；对于变化数据，用户可设置OLR-WA使其更快适应变化或抵抗变化。