SVM-Based Classifier Design with Controlled Confidence

Authors:
Mingkun Li;Ishwar K. Sethi
Affiliations:
Oakland University, Rochester, MI;Oakland University, Rochester, MI
Venue:
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 1 - Volume 01
Year:
2004

Citing 0
Cited 3

Confidence-Based Active Learning

IEEE Transactions on Pattern Analysis and Machine Intelligence
Confidence based active learning for whole object image segmentation

MRCS'06 Proceedings of the 2006 international conference on Multimedia Content Representation, Classification and Security
Confidence-Based color modeling for online video segmentation

ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part II

Quantified Score

Hi-index	0.00

Visualization

Abstract

A new classification methodology with controlled error rates and a reject option is proposed in this paper. The proposed methodology is implemented using Support Vector Machine's (SVM's) posterior probability preserving property. A new nonparametric method is proposed to accurately estimate error rates from the output of a trained SVM. The experimental results clearly demonstrate the efficacy of the suggested classifier design methodology.