Original Contribution: Stacked generalization
Neural Networks
Machine Learning
Ensemble Methods in Machine Learning
MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Face Description with Local Binary Patterns: Application to Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pegasos: Primal Estimated sub-GrAdient SOlver for SVM
Proceedings of the 24th international conference on Machine learning
Maximum Relative Margin and Data-Dependent Regularization
The Journal of Machine Learning Research
Simultaneous training of negatively correlated neural networks inan ensemble
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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When the description of the visual data is rich and consists of many features, a classification based on a single model can often be enhanced using an ensemble of models. We suggest a new ensemble learning method that encourages the base classifiers to learn different aspects of the data. Initially, a binary classification algorithm such as Support Vector Machine is applied and its confidence values on the training set are considered. Following the idea that ensemble methods work best when the classification errors of the base classifiers are not related, we serially learn additional classifiers whose output confidences on the training examples are minimally correlated. Finally, these uncorrelated classifiers are assembled using the GentleBoost algorithm. Presented experiments in various visual recognition domains demonstrate the effectiveness of the method.