Latent-lSVM classification of very high-dimensional and large-scale multi-class datasets.
Thanh-Nghi DoFrançois PouletPublished in: Concurr. Comput. Pract. Exp. (2019)
Keyphrases
- multi class
- binary and multi class
- multiclass classification
- multi class classification
- high dimensional
- binary classification problems
- support vector machine
- multi class classifier
- binary classifiers
- multi class boosting
- binary classification tasks
- cost sensitive
- multiple classes
- class probabilities
- multi class svm
- multiclass problems
- multi class svms
- multi class classifiers
- feature space
- svm classification
- multi class problems
- error correcting output codes
- feature selection
- classification accuracy
- binary classification
- single class
- improve the classification accuracy
- protein classification
- object detection
- kernel logistic regression
- high dimensionality
- support vector machine svm
- low dimensional
- dimensionality reduction
- training set
- feature extraction
- data mining
- probabilistic boosting tree
- machine learning
- base classifiers
- image classification
- support vector
- multi label classification
- class imbalance
- multi task
- svm classifier
- benchmark datasets
- training samples
- supervised learning
- feature vectors
- pairwise
- decision trees