Absolute error bounds for learning linear functions online

In this note, we consider the problem of learning a linear function from ** to ** online. Two previous algorithms have been presented which achieve an optimal sum of squared error terms. We show that neither algorithm performs well with respect to absolute error. We also show that in both problem settings, very simple ideas given an algorithm which achieves optimal or close to optimal worst case absolute error.