Manipulation of prior probabilities in support vector classifications

Cawley, Gavin C. ORCID: and Talbot, Nicola L. C. (2001) Manipulation of prior probabilities in support vector classifications. In: 2001 International Joint Conference on Neural Networks, 2001-07-15 - 2001-07-19.

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Asymmetric margin error costs for positive and negative examples are often cited as an efficient heuristic compensating for unrepresentative priors in training support vector classifiers. In this paper we show that this heuristic is well justified via simple re-sampling ideas applied to the dual Lagrangian defining the 1-norm soft-margin support vector machine. This observation also provides a simple expression for the asymptotically optimal ratio of margin error penalties, eliminating the need for the trial-and-error experimentation normally encountered. This method allows the use of a smaller, balanced training data set in problems characterised by widely disparate prior probabilities, reducing the training time. The usefulness of this method is then demonstrated on a real world benchmark problem

Item Type: Conference or Workshop Item (Paper)
Faculty \ School: Faculty of Science > School of Computing Sciences

UEA Research Groups: Faculty of Science > Research Groups > Computational Biology
Faculty of Science > Research Groups > Data Science and Statistics
Faculty of Science > Research Groups > Centre for Ocean and Atmospheric Sciences
Depositing User: Vishal Gautam
Date Deposited: 28 Jul 2011 15:53
Last Modified: 22 Apr 2023 03:35
DOI: 10.1109/IJCNN.2001.938748

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