K-means based on active learning for support vector machine.
Jie GanAng LiQian-Lin LeiHao RenYun YangPublished in: ICIS (2017)
Keyphrases
- active learning
- support vector machine
- k means
- training set
- machine learning
- statistical learning theory
- clustering algorithm
- multi class
- selective sampling
- imbalanced data classification
- svm classifier
- learning strategies
- random sampling
- sample selection
- data clustering
- semi supervised
- support vector machine svm
- learning algorithm
- training data
- feature selection
- spectral clustering
- kernel methods
- support vector
- learning process
- decision boundary
- self organizing maps
- stock market prediction
- multi class support vector machines
- clustering method
- expectation maximization
- unsupervised clustering
- soft margin
- labeled data
- radial basis function
- hierarchical clustering
- cost sensitive
- pool based active learning
- small sample
- decision forest
- neural network
- training examples
- semi supervised learning
- supervised learning
- feature vectors
- experimental design
- hyperplane
- fuzzy c means
- unlabeled data
- fuzzy clustering algorithm
- relevance feedback
- rough k means
- feature space