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Thesis Defense - Reid


Model Combination in Multiclass Classification
Computer Science PhD Candidate

Multiclass classification is an important machine learning problem that involves classifying a pattern into one of several classes, and encompasses domains such as handwritten character recognition, protein structure classification, heartbeat arrhythmia identification and many others. In this thesis, we investigate three issues in combining models to perform multiclass classification. First, we demonstrate that regularization is essential in linear combinations of multiclass classifiers. Second, we show that when solving a multiclass problem using a combination of binary classifiers, it is more effective to share hyperparameters across models than to optimize them independently. Third, we introduce a new method for combining binary pairwise classifiers that overcomes several problems with existing pairwise classification schemes and exhibits significantly better performance on many problems. Our contributions span the themes of model selection and reduction from multiclass to binary classification.

Committee: Michael Mozer, Professor (Chair)
Gregory Grudic, FlashBack Technologies
Richard Byrd, Professor
James Martin, Professor
François Meyer, Department of Electrical and Computer Engineering

See also:
Department of Computer Science
College of Engineering and Applied Science
University of Colorado Boulder
Boulder, CO 80309-0430 USA
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