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Exploiting diversity of margin-based classifiers
2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541)
An experimental comparison among Support Vector Machines, AdaBoost and a recently proposed model for maximizing the margin with Feed-forward Neural Networks has been made on a real-world classification problem, namely Text Categorization. The results obtained when comparing their agreement on the predictions show that similar performance does not imply similar predictions, suggesting that different models can be combined to obtain better performance. As a consequence of the study, we derived a
doi:10.1109/ijcnn.2004.1379942
fatcat:ojnvucoqifavlbcccntt2alpna