UAI '88 Proceedings of the Fourth Annual Conference on Uncertainty in Artificial Intelligence
Solving multiclass learning problems via error-correcting output codes
Journal of Artificial Intelligence Research
ADORE: Adaptive Object Recognition
ICVS '99 Proceedings of the First International Conference on Computer Vision Systems
Investigating methods for improving bagged k-NN classifiers
AIKED'08 Proceedings of the 7th WSEAS International Conference on Artificial intelligence, knowledge engineering and data bases
Actively exploring creation of face space(s) for improved face recognition
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
A comparison of soft fusion methods under different bagging scenarios
CIMMACS'07 Proceedings of the 6th WSEAS international conference on Computational intelligence, man-machine systems and cybernetics
Many are better than one: improving probabilistic estimates from decision trees
MLCW'05 Proceedings of the First international conference on Machine Learning Challenges: evaluating Predictive Uncertainty Visual Object Classification, and Recognizing Textual Entailment
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Previous research has shown that aggregated predictors improve the performance of non-parametric function approximation techniques. This paper presents the results of applying aggregated predictors to a computer vision problem, and shows that the method of bagging significantly improves performance. In fact, the results are better than those previously reported on other domains. This paper explains this performance in terms of the variance and bias.