On the Rate of Convergence of Regularized Boosting Classifiers.
Gilles BlanchardGábor LugosiNicolas VayatisPublished in: J. Mach. Learn. Res. (2003)
Keyphrases
- randomized trees
- weak classifiers
- ensemble learning
- improving classification accuracy
- ensemble classifier
- feature selection
- weak learners
- boosting algorithms
- boosting framework
- adaboost algorithm
- decision stumps
- majority voting
- training data
- accurate classifiers
- strong classifier
- multiple classifier systems
- classification trees
- machine learning algorithms
- weighted voting
- naive bayes
- support vector
- least squares
- classification systems
- boosted classifiers
- ensemble classification
- bayesian classifiers
- multi class
- multiclass classification
- learning algorithm
- classification models
- decision trees
- discriminative classifiers
- svm classifier
- test set
- support vector machine
- active learning
- training examples
- training samples
- machine learning
- binary classification problems
- binary classifiers
- linear classifiers
- random forests
- generalization ability
- base classifiers
- ensemble methods
- classification algorithm
- class labels
- face detection
- linear combination
- training set