Perceptron Mistake Bounds

This work provides incremental improvements in theoretical machine learning by extending mistake bounds for the Perceptron algorithm to more general loss functions.

The authors tackled the problem of deriving mistake bounds for the Perceptron algorithm by introducing novel bounds that generalize beyond standard margin-loss types, allow for any convex and Lipschitz loss function, and feature a simple proof.