Fast learning rates in statistical inference through aggregation
Jean-Yves Audibert · The Annals of Statistics · 2009
We develop minimax optimal risk bounds for the general learning task consisting in predicting as well as the best function in a reference set $\mathcal{G}$ up to the smallest possible additive term, called the convergence rate. When the reference set is finite and when n denotes the size of the training data, we provide minimax convergence rates of the form $C(\frac{\log|\mathcal{G}|}{n})^{v}$ with tight evaluation of the positive constant C and with exact 0