For learning with monotone adversaries, the worst-case minimax expected error is Θ(1/n) in dimension one and Θ((d/n) log(n/d)) in dimension two or more.
Learning Quickly When Irrelevant Attributes Abound: A New Linear-Threshold Algorithm
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Optimal Rates for Learning with Monotone Adversaries
For learning with monotone adversaries, the worst-case minimax expected error is Θ(1/n) in dimension one and Θ((d/n) log(n/d)) in dimension two or more.