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IJCNN
2006
IEEE
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 insensitiv...
Matthew Boardman, Thomas P. Trappenberg
FLAIRS
2003
13 years 6 months ago
Optimizing F-Measure with Support Vector Machines
Support vector machines (SVMs) are regularly used for classification of unbalanced data by weighting more heavily the error contribution from the rare class. This heuristic techn...
David R. Musicant, Vipin Kumar, Aysel Ozgur
ACML
2009
Springer
13 years 9 months ago
Max-margin Multiple-Instance Learning via Semidefinite Programming
In this paper, we present a novel semidefinite programming approach for multiple-instance learning. We first formulate the multipleinstance learning as a combinatorial maximum marg...
Yuhong Guo
ISNN
2005
Springer
13 years 11 months ago
Scaling the Kernel Function to Improve Performance of the Support Vector Machine
Abstract. The present study investigates a geometrical method for optimizing the kernel function of a support vector machine. The method is an improvement of the one proposed in [4...
Peter Williams, Sheng Li, Jianfeng Feng, Si Wu
ESANN
2000
13 years 6 months ago
Algorithmic approaches to training Support Vector Machines: a survey
: Support Vector Machines (SVMs) have become an increasingly popular tool for machine learning tasks involving classi cation, regression or novelty detection. They exhibit good gen...
Colin Campbell