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Supervised learning with random labelling errors

Bootkrajang, Jakramate (2013)
Ph.D. thesis, University of Birmingham.

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Classical supervised learning from a training set of labelled examples assumes that the labels are correct. But in reality labelling errors may originate, for example, from human mistakes, diverging human opinions, or errors of the measuring instruments. In such cases the training set is misleading and in consequence the learning may suffer.

In this thesis we consider probabilistic modelling of random label noise. The goal of this research is two-fold. First, to develop new improved algorithms and architectures from a principled footing which are able to detect and bypass the unwanted effects of mislabelling. Second, to study the performance of such methods both empirically and theoretically. We build upon two classical probabilistic classifiers, the normal discriminant analysis and the logistic regression and introduce the label-noise robust versions of these classifiers.
We also develop useful extensions such as a sparse extension and a kernel extension in order to broaden applicability of the robust classifiers. Finally, we devise an ensemble of the robust classifiers in order to understand how the robust models perform collectively.

Theoretical and empirical analysis of the proposed models show that the new robust models are superior to the traditional approaches in terms of parameter estimation and classification performance.

Type of Work:Ph.D. thesis.
Supervisor(s):Kaban, Ata
School/Faculty:Colleges (2008 onwards) > College of Engineering & Physical Sciences
Department:School of Computer Science
Subjects:QA75 Electronic computers. Computer science
Institution:University of Birmingham
ID Code:4487
This unpublished thesis/dissertation is copyright of the author and/or third parties. The intellectual property rights of the author or third parties in respect of this work are as defined by The Copyright Designs and Patents Act 1988 or as modified by any successor legislation. Any use made of information contained in this thesis/dissertation must be in accordance with that legislation and must be properly acknowledged. Further distribution or reproduction in any format is prohibited without the permission of the copyright holder.
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