Online Learning of Multiple Tasks and Their Relationships : Testing on Spam Email Data and EEG Signals Recorded in Construction Fields
This work addresses the problem of adapting task relationships in sequential data processing for applications like spam filtering and EEG analysis, representing an incremental improvement over existing methods.
The paper tackled online multi-task learning by developing a method that updates task relatedness iteratively, outperforming a conventional fixed-relatedness approach with accuracy improvements of 1% to 3% on EEG data and maintaining low error rates around 12% on spam data.
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.