  We prove new fast learning rates for the one-vs-all multiclass plug-in
classifiers trained either from exponentially strongly mixing data or from data
generated by a converging drifting distribution. These are two typical
scenarios where training data are not iid. The learning rates are obtained
under a multiclass version of Tsybakov's margin assumption, a type of low-noise
assumption, and do not depend on the number of classes. Our results are general
and include a previous result for binary-class plug-in classifiers with iid
data as a special case. In contrast to previous works for least squares SVMs
under the binary-class setting, our results retain the optimal learning rate in
the iid case.
