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ASME Press Select Proceedings

Intelligent Engineering Systems Through Artificial Neural Networks, Volume 17

Editor
C. H. Dagli
C. H. Dagli
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ISBN-10:
0791802655
No. of Pages:
650
Publisher:
ASME Press
Publication date:
2007

It is common to train a classifier with a training set, and to test it with a testing set to study the classification accuracy. In this paper, we show how to effectively use a number of validation sets obtained from the original training data to improve the performance of a classifier. The proposed validation boosting algorithm is illustrated with a support vector machine (SVM) in the application of lymphography classification. A number of runs with the algorithm is generated to show its robustness as well as to generate consensus results. At each run, a number of validation datasets are generated...

Abstract
Introduction
Dataset
Support Vector Machines
Training and Validation Resampling Technique
Experiments
Discussion and Conclusions
Acknowledgement
References
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