Explaining the Success of AdaBoost and Random Forests as Interpolating Classifiers.
Abraham J. WynerMatthew OlsonJustin BleichDavid MeasePublished in: J. Mach. Learn. Res. (2017)
Keyphrases
- random forests
- decision trees
- ensemble methods
- decision tree ensembles
- base learners
- machine learning algorithms
- randomized trees
- majority voting
- accurate classifiers
- random forest
- ensemble learning
- ensemble classifier
- classifier ensemble
- machine learning methods
- logistic regression
- learning algorithm
- training data
- support vector
- naive bayes
- tree ensembles
- prediction accuracy
- base classifiers
- boosting algorithms
- adaboost algorithm
- training set
- weak classifiers
- machine learning
- meta learning
- classification trees
- feature selection
- benchmark datasets
- training samples
- multi class
- object detection
- subspace methods
- feature set
- face detection
- data mining
- active learning
- binary classifiers
- support vector machine
- supervised learning
- data sets