Constant Rate Approximate Maximum Margin Algorithms
Tsampouka, Petroula and Shawe-Taylor, John (2006) Constant Rate Approximate Maximum Margin Algorithms.
We present a new class of perceptron-like algorithms with margin in which the “effective” learning rate, defined as the ratio of the learning rate to the length of the weight vector, remains constant. We prove that the new algorithms converge in a finite number of steps and show that there exists a limit of the parameters involved in which convergence leads to classification with maximum margin.
|Item Type:||Monograph (Technical Report)|
|Keywords:||online learning, maximum margin classifiers|
|Divisions:||Faculty of Physical Sciences and Engineering > Electronics and Computer Science
|Date Deposited:||26 Jan 2006|
|Last Modified:||31 Mar 2016 14:04|
|Further Information:||Google Scholar|
|RDF:||RDF+N-Triples, RDF+N3, RDF+XML, Browse.|
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