Original Contribution: Stacked generalization
Neural Networks
The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
Neural Network-Based Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Example-Based Learning for View-Based Human Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Bias/variance decompositions for likelihood-based estimators
Neural Computation
Boosted mixture of experts: an ensemble learning scheme
Neural Computation
Detecting Faces in Images: A Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Support Vector Machine Ensemble with Bagging
SVM '02 Proceedings of the First International Workshop on Pattern Recognition with Support Vector Machines
Training Support Vector Machines: an Application to Face Detection
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Multi-Modal System for Locating Heads and Faces
FG '96 Proceedings of the 2nd International Conference on Automatic Face and Gesture Recognition (FG '96)
Extracting Gestural Motion Trajectories
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
The Journal of Machine Learning Research
Bias-Variance Analysis of Support Vector Machines for the Development of SVM-Based Ensemble Methods
The Journal of Machine Learning Research
On the Stability and Bias-Variance Analysis of Kernel Matrix Learning
CAI '07 Proceedings of the 20th conference of the Canadian Society for Computational Studies of Intelligence on Advances in Artificial Intelligence
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In this paper, we deal with the stability of support vector machines (SVMs) in classification tasks. We decompose the average prediction error of SVMs into the bias and the variance terms, and we define the aggregation effect. By estimating the aforementioned terms with bootstrap smoothing techniques, we demonstrate that support vector machines are stable classifiers. To investigate the stability of the SVM several experiments were conducted. The first experiment deals with face detection. The second experiment conducted is related to the binary classification of three artificially generated data sets stemming from known distributions and an additional synthetic data set known as "Waveform". Finally, in order to support our claim on the stability of SVMs, two more binary classification experiments were carried out on the "Pime Indian Diabetes" and the "Wisconsin Breast Cancer" data sets. In general, bagging is not expected to improve the classification accuracy of SVMs.