Sparse least-squares Universum twin bounded support vector machine with adaptive Lp-norms and feature selection.
Hossein MoosaeiFatemeh BazikarMilan HladíkPanos M. PardalosPublished in: Expert Syst. Appl. (2024)
Keyphrases
- support vector machine
- feature selection
- least squares
- binary classification
- maximum margin
- unbalanced data
- semi supervised learning
- support vector
- multi class
- machine learning
- svm classifier
- text categorization
- feature space
- training data
- linear programming
- reduced set
- support vector machine svm
- hyperplane
- cost sensitive
- kernel methods
- linear program
- semi definite programming
- high dimensional
- feature set
- classification accuracy
- unlabeled data
- feature vectors
- unsupervised learning
- feature extraction
- knn
- support vectors
- ls svm
- training set
- optical flow
- semidefinite programming
- primal dual
- multi task
- linear regression
- kernel function
- decision boundary
- semi supervised
- selected features
- misclassification costs
- supervised learning
- naive bayes
- labeled data