Sparse least squares support vector training in the reduced empirical feature space.
Shigeo AbePublished in: Pattern Anal. Appl. (2007)
Keyphrases
- least squares
- feature space
- support vector
- kernel function
- high dimensional
- training set
- sparse linear
- training samples
- training examples
- classification accuracy
- hyperplane
- learning machines
- reduced set
- support vector machine
- ls svm
- linear discriminant analysis
- sparse coding
- kernel methods
- generalization ability
- stochastic gradient descent
- avoid overfitting
- feature selection
- training process
- input space
- support vectors
- canonical correlation analysis
- robust estimation
- high dimensional data
- high dimension
- training algorithm
- linear svm
- polynomial kernels
- mercer kernels
- mean shift
- logistic regression
- image representation
- low dimensional
- dimensionality reduction
- feature vectors
- dimension reduction
- cross validation
- svm classifier
- machine learning
- high dimensional feature space
- optical flow
- data sets
- decision trees
- feature extraction
- standard svm
- svm training
- decision function
- input data
- sparse representation
- high dimensionality