Bagging and Boosting for the Nearest Mean Classifier: Effects of Sample Size on Diversity and Accuracy.
Marina SkurichinaLudmila KunchevaRobert P. W. DuinPublished in: Multiple Classifier Systems (2002)
Keyphrases
- sample size
- variance reduction
- ensemble members
- ensemble methods
- generalization error
- progressive sampling
- ensemble learning
- individual classifiers
- decision stumps
- small sample
- classifier ensemble
- weak learners
- weak classifiers
- model selection
- ensemble classifier
- leave one out cross validation
- majority voting
- random sampling
- base classifiers
- decision trees
- statistical power
- prediction accuracy
- upper bound
- small samples
- feature selection
- confidence intervals
- generalization ability
- statistical hypothesis testing
- pac learning
- training set
- learning algorithm
- randomized trees
- base learners
- nearest neighbor
- multiple classifier systems
- training data
- random forest
- boosting algorithms
- sample complexity
- feature space
- induction algorithms
- naive bayes classifier
- vc dimension
- random forests
- frequent itemsets