Sparse Least Squares Support Vector Regressors Trained in the Reduced Empirical Feature Space.
Shigeo AbeKenta OnishiPublished in: ICANN (2) (2007)
Keyphrases
- least squares
- feature space
- support vector
- kernel function
- svm classifier
- sparse linear
- high dimensional
- training set
- support vector machine
- classification accuracy
- hyperplane
- relevance vector machine
- kernel methods
- ls svm
- high dimension
- feature selection
- reduced set
- sparse coding
- feature vectors
- mercer kernels
- canonical correlation analysis
- linear regression
- cross validation
- regression problems
- support vector machine svm
- mean shift
- input space
- training samples
- optical flow
- linear discriminant analysis
- dimensionality reduction
- robust estimation
- sparse matrix
- high dimensionality
- principal component analysis
- logistic regression
- sparse representation
- low dimensional
- training examples
- polynomial kernels
- kernel matrix
- gaussian kernels
- feature extraction
- training process
- dimension reduction
- data points
- multi class
- learning machines
- image retrieval
- hyperparameters
- training data
- high dimensional feature space
- image classification
- image representation
- standard svm
- loss function
- machine learning