Performance Bounds for Single Layer Threshold Networks when Tracking a Drifting Adversary
This paper finds upper bounds for the generalization error of three tracking algorithms when confronted with a worst case adversary. A system identification model is used where both the target and tracking network are single layer threshold networks, with the target weights changing slowly (the drift problem). Previous work considered random unbiased drifting adversaries. This paper focuses on the analysis of a worst case drifting adversary. For a small drift rate of ?, we find that upper bounds for the optimal conservative tracker, the perceptron tracker, and the least mean square (LMS) tracker are respectively
generalization error system identification time-varying systems neural networks adaptive systems tracking drift