Sparse LS-SVM in the Sorted Empirical Feature Space for Pattern Classification.
Takuya KitamuraKohei AsanoPublished in: ICONIP (1) (2015)
Keyphrases
- pattern classification
- ls svm
- feature space
- least squares support vector machine
- high dimensional
- high dimension
- feature extraction
- support vector machine
- variable selection
- least squares
- pattern recognition
- sparse coding
- sparse representation
- cross validation
- high dimensionality
- dimension reduction
- input variables
- feature vectors
- dimensionality reduction
- support vector
- prediction model
- image representation
- generalization ability
- kernel function
- linear discriminant analysis
- feature selection
- principal component analysis
- input space
- data points
- improved algorithm
- radial basis function
- training samples
- statistical learning theory
- rbf neural network
- parameter estimation
- correlation coefficient
- svm classifier
- neural network
- support vector machine svm
- low dimensional
- training set
- model selection
- hyperparameters
- feature set
- artificial neural networks
- learning algorithm
- data mining