Aspects of Online Learning
Date
2004
Authors
Harrington, Edward
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Abstract
Online learning algorithms have several key advantages compared to their batch learning algorithm counterparts. This thesis investigates several online learning algorithms and their application. The thesis has an underlying theme of the idea of combining several simple algorithms to give better performance. In this thesis we investigate: combining weights, combining hypothesis, and (sort of) hierarchical combining.¶ ...
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Keywords
online learning algorithms, perceptron, large margin classifiers, Bayes point machine, BPM, online Bayes point machine, OBPM, tracking experts, fixed share hierarchy algorithm, FSH, channel equalization, equalizer, line voting, ranking algorithms
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Thesis (PhD)