CUED Publications database

Learning from non-iid data: Fast rates for the one-vs-all multiclass plug-in classifiers

Dinh, V and Ho, LST and Cuong, NV and Nguyen, D and Nguyen, BT (2015) Learning from non-iid data: Fast rates for the one-vs-all multiclass plug-in classifiers. In: UNSPECIFIED pp. 375-387..

Full text not available from this repository.

Abstract

© Springer International Publishing Switzerland 2015. 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.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Subjects: UNSPECIFIED
Divisions: Div F > Computational and Biological Learning
Depositing User: Cron Job
Date Deposited: 17 Jul 2017 19:37
Last Modified: 17 Aug 2017 01:25
DOI: