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17th International Conference on Pattern Recognition (ICPR'04) - Volume 1
Comparing Optimal Bounding Ellipsoid and Support Vector Machine Active Learning
Cambridge UK
August 23-August 26
ISBN: 0-7695-2128-2
Ibrahim Gokcen, Tulane University, New Orleans, LA
Dale Joachim, Tulane University, New Orleans, LA
Jack R. Deller, Michigan State University, East Lansing, MI
In this paper we propose two active learning algorithms combining statistical active learning methods based on SVM and optimal bounding algorithms (OBE) of adaptive system identification. We unify SVM and OBE by demonstrating the similarities and representing SVM in an OBE interpretation. Samples are judiciously selected based on a volume measure provided by OBE using both simple heuristic and greedy optimal strategies. Preliminary experiments illustrate the effectiveness of the proposed algorithms as compared to similar methods.
Citation:
Ibrahim Gokcen, Dale Joachim, Jack R. Deller, "Comparing Optimal Bounding Ellipsoid and Support Vector Machine Active Learning," icpr, vol. 1, pp.172-175, 17th International Conference on Pattern Recognition (ICPR'04) - Volume 1, 2004
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