Comparing Optimal Bounding Ellipsoid and Support Vector Machine Active Learning

Ibrahim Gokcen; Dale Joachim; Jack R. Deller

doi:10.1109/ICPR.2004.1334041

Pattern Recognition, International Conference on

Comparing Optimal Bounding Ellipsoid and Support Vector Machine Active Learning

Year: 2004, Volume: 2, Pages: 172-175

DOI Bookmark: 10.1109/ICPR.2004.1334041

Authors

Ibrahim Gokcen, Tulane University, New Orleans, LA
Dale Joachim, Tulane University, New Orleans, LA
Jack R. Deller, Michigan State University, East Lansing, MI

Abstract

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.

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Comparing Optimal Bounding Ellipsoid and Support Vector Machine Active Learning

Authors

Abstract

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