Asymptotically Optimal Choice of #epsilon#-Loss for Support Vector Machines

摘要

Under the assumption of asymptotically unbiased estimators we show that there exists a nontrivial choice of the insensitivity parameter in Vapnik's #epsilon#-insensitive loss function which scales linearly with the input noise of the training data. This finding is backed by experimental results.