Online Bayes Point Machines
Date
2003
Authors
Harrington, Edward
Herbrich, Ralf
Kivinen, Jyrki
Platt, John C
Williamson, Robert
Journal Title
Journal ISSN
Volume Title
Publisher
Springer
Abstract
We present a new and simple algorithm for learning large margin classifiers that works in a truly online manner. The algorithm generates a linear classifier by averaging the weights associated with several perceptron-like algorithms run in parallel in order to approximate the Bayes point. A random subsample of the incoming data stream is used to ensure diversity in the perceptron solutions. We experimentally study the algorithm's performance on online and batch learning settings. The online experiments showed that our algorithm produces a low prediction error on the training sequence and tracks the presence of concept drift. On the batch problems its performance is comparable to the maximum margin algorithm which explicitly maximises the margin.
Description
Keywords
Keywords: Batch learning settings; Bayes point machines; Linear classifiers; Training sequence; Algorithms; Approximation theory; Error analysis; Learning systems; Neural networks; Problem solving; Classification (of information)
Citation
Collections
Source
Advances in Knowledge Discovery and Data Mining: Proceedings of 7th Pacific-Asia Conference, PAKDD 2003
Type
Conference paper