Boosting Classifiers with Noisy Inference.
Yongjune KimYuval CassutoLav R. VarshneyPublished in: CoRR (2019)
Keyphrases
- ensemble learning
- weak classifiers
- randomized trees
- weak learners
- improving classification accuracy
- ensemble classifier
- boosting algorithms
- boosting framework
- feature selection
- majority voting
- decision stumps
- accurate classifiers
- adaboost algorithm
- strong classifier
- support vector
- classification systems
- weighted voting
- linear classifiers
- training data
- ensemble methods
- multiple classifier systems
- probabilistic inference
- bayesian networks
- decision trees
- ensemble classification
- boosted classifiers
- classifier combination
- base classifiers
- svm classifier
- machine learning algorithms
- bayesian classifiers
- multiclass classification
- combining classifiers
- learning algorithm
- machine learning methods
- training examples
- naive bayes
- feature set
- multi class
- classification trees
- classification models
- multiple classifiers
- cost sensitive
- noisy data
- test set
- face detection
- training samples
- support vector machine
- training set