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IJCNN
2006
IEEE

A Heuristic for Free Parameter Optimization with Support Vector Machines

13 years 11 months ago
A Heuristic for Free Parameter Optimization with Support Vector Machines
— A heuristic is proposed to address free parameter selection for Support Vector Machines, with the goals of improving generalization performance and providing greater insensitivity to training set selection. The many local extrema in these optimization problems make gradient descent algorithms impractical. The main point of the proposed heuristic is the inclusion of a model complexity measure to improve generalization performance. We also use simulated annealing to improve parameter search efficiency compared to an exhaustive grid search, and include an intensity-weighted centre of mass of the most optimum points to reduce volatility. We examine two standard classification problems for comparison, and apply the heuristic to bioinformatics and retinal electrophysiology classification.
Matthew Boardman, Thomas P. Trappenberg
Added 11 Jun 2010
Updated 11 Jun 2010
Type Conference
Year 2006
Where IJCNN
Authors Matthew Boardman, Thomas P. Trappenberg
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