Signal theory for SVM kernel design with applications to parameter estimation and sequence kernels.
James D. B. NelsonRobert I. DamperSteve R. GunnBaofeng GuoPublished in: Neurocomputing (2008)
Keyphrases
- parameter estimation
- kernel function
- support vector
- maximum likelihood
- kernel methods
- model selection
- em algorithm
- least squares
- feature space
- multiple kernel
- markov random field
- multiple kernel learning
- support vector machine
- least squares support vector machine
- ls svm
- svm classification
- random fields
- kernel parameters
- parameter values
- expectation maximization
- svm classifier
- kernel machines
- support vector machine svm
- model fitting
- gaussian kernel
- parameter estimates
- parameter estimation algorithm
- support vectors
- reproducing kernel hilbert space
- polynomial kernels
- positive definite
- rbf kernel
- computer vision
- histogram intersection kernel
- estimation problems
- kernel matrix
- graphical models
- probabilistic model
- feature selection