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ICML
2003
IEEE

Low Bias Bagged Support Vector Machines

14 years 4 months ago
Low Bias Bagged Support Vector Machines
Theoretical and experimental analyses of bagging indicate that it is primarily a variance reduction technique. This suggests that bagging should be applied to learning algorithms tuned to minimize bias, even at the cost of some increase in variance. We test this idea with Support Vector Machines (SVMs) by employing out-of-bag estimates of bias and variance to tune the SVMs. Experiments indicate that bagging of low-bias SVMs (the "lobag" algorithm) never hurts generalization performance and often improves it compared with well-tuned single SVMs and to bags of individually well-tuned SVMs.
Giorgio Valentini, Thomas G. Dietterich
Added 17 Nov 2009
Updated 17 Nov 2009
Type Conference
Year 2003
Where ICML
Authors Giorgio Valentini, Thomas G. Dietterich
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