This paper examines an online multi-task learning (OMTL) method, which processes data sequentially to predict labels across related tasks. The framework learns task weights and their relatedness concurrently. Unlike previous models that assumed static task relatedness, our approach treats tasks as initially independent, updating their relatedness iteratively using newly calculated weight vectors. We introduced three rules to update the task relatedness matrix: OMTLCOV, OMTLLOG, and OMTLVON, and compared them against a conventional method (CMTL) that uses a fixed relatedness value. Performance evaluations on three datasets a spam dataset and two EEG datasets from construction workers under varying conditions demonstrated that our OMTL methods outperform CMTL, improving accuracy by 1\% to 3\% on EEG data, and maintaining low error rates around 12\% on the spam dataset.

翻译：本文研究了一种在线多任务学习（OMTL）方法，该方法通过顺序处理数据来预测相关任务的标签。该框架同时学习任务权重及其关联性。与先前假设任务关联性静态不变的模型不同，我们的方法将任务视为初始独立的，并利用新计算的权重向量迭代更新其关联性。我们引入了三种更新任务关联矩阵的规则：OMTLCOV、OMTLLOG和OMTLVON，并将其与使用固定关联值的传统方法（CMTL）进行了比较。在三个数据集上的性能评估——一个垃圾邮件数据集和两个来自不同条件下建筑工人的脑电图数据集——表明我们的OMTL方法优于CMTL，在脑电图数据上将准确率提高了1%至3%，并在垃圾邮件数据集上保持了约12%的低错误率。