Online Bayes Point Machines
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...[Show more]
|Collections||ANU Research Publications|
|Source:||Advances in Knowledge Discovery and Data Mining: Proceedings of 7th Pacific-Asia Conference, PAKDD 2003|
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