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 binaryclass 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.
CITATION STYLE
Dinh, V., Ho, L. S. T., Cuong, N. V., Nguyen, D., & Nguyen, B. T. (2015). Learning from non-iid data: Fast rates for the one-vs-all multiclass plug-in classifiers. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9076, pp. 375–387). Springer Verlag. https://doi.org/10.1007/978-3-319-17142-5_32
Mendeley helps you to discover research relevant for your work.