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Constant Rate Approximate Maximum Margin Algorithms

Tsampouka, Petroula and Shawe-Taylor, John (2006) Constant Rate Approximate Maximum Margin Algorithms s.n.

Record type: Monograph (Project Report)

Abstract

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.

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More information

Published date: 2006
Keywords: online learning, maximum margin classifiers
Organisations: Electronics & Computer Science

Identifiers

Local EPrints ID: 261832
URI: http://eprints.soton.ac.uk/id/eprint/261832
PURE UUID: c021d6af-2445-4046-b41b-c300429a220d

Catalogue record

Date deposited: 26 Jan 2006
Last modified: 18 Jul 2017 08:58

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Contributors

Author: Petroula Tsampouka
Author: John Shawe-Taylor

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