Subspace-Based L2 Support Vector Machines.
Takuya KitamuraShigeo AbePublished in: Aust. J. Intell. Inf. Process. Syst. (2010)
Keyphrases
- support vector
- learning machines
- large margin classifiers
- kernel methods
- cross validation
- dimensionality reduction
- clustering high dimensional data
- logistic regression
- kernel function
- support vector machine
- multi class
- loss function
- multi class classification
- binary classification
- low dimensional
- neural network
- generalization ability
- subspace clustering
- principal component analysis
- feature space
- machine learning
- classification accuracy
- subspace learning
- high dimensional
- learning algorithm
- feature selection
- training examples
- decision function
- generalization bounds
- svm classification
- linear subspace
- image sequences
- high dimensional data
- hyperplane
- principal components
- radial basis function
- feature extraction
- data sets