Creating an ensemble of diverse support vector machines using Adaboost.
Naiyan Hari Cândido LimaAdrião Duarte Dória NetoJorge Dantas de MeloPublished in: IJCNN (2009)
Keyphrases
- support vector
- learning machines
- ensemble learning
- large margin classifiers
- ensemble methods
- generalization ability
- base classifiers
- weak learners
- feature selection
- learning algorithm
- multi class
- regression problems
- multi class classification
- kernel function
- support vector machine
- generalization bounds
- binary classification
- training data
- logistic regression
- loss function
- cross validation
- classifier ensemble
- random forests
- strong classifier
- ensemble classifier
- base learners
- real world
- support vectors
- training set
- svm classifier
- wide variety
- prediction accuracy
- random forest
- training examples
- hyperplane
- decision trees
- weak classifiers
- radial basis function
- decision stumps
- classification accuracy
- benchmark datasets
- pruning algorithm
- majority voting
- object detection
- neural network
- adaboost algorithm
- binary classification problems
- feature ranking
- boosting algorithms
- maximum margin
- machine learning methods
- data sets