The nature of statistical learning theory
The nature of statistical learning theory
Ensembling neural networks: many could be better than all
Artificial Intelligence
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
Bias-Variance Analysis of Support Vector Machines for the Development of SVM-Based Ensemble Methods
The Journal of Machine Learning Research
A Comparison of Several Ensemble Methods for Text Categorization
SCC '04 Proceedings of the 2004 IEEE International Conference on Services Computing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Combining active learning and semi-supervised for improving learning performance
Proceedings of the 4th International Symposium on Applied Sciences in Biomedical and Communication Technologies
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This paper proposes two novel ensemble algorithms for training support vector machines based on constraint projection technique and selective ensemble strategy. Firstly, projective matrices are determined upon randomly selected must-link and cannot-link constraint sets, with which original training samples are transformed into different representation spaces to train a group of base classifiers. Then, two selective ensemble techniques are used to learn the best weighting vector for combining them, namely genetic optimization and minimizing deviation errors respectively. Experiments on UCI datasets show that both proposed algorithms improve the generalization performance of support vector machines significantly, which are much better than classical ensemble algorithms, such as Bagging, Boosting, feature Bagging and LoBag.