We show in section 3.4 that the widely used Freund and Schapire (1999) voted perceptron algorithm could face a constant hard case bias when confronted with annotation noise in training data, irrespective of the size of the dataset.
e(ζ 2 ) ≥ min{H T (δ), H γ (δ)}, and Corollary 4 The bound in theorem 2 does not converge to zero for large N .