Kernel-Based Machine Learning Lab
Class "onlearn"
Assing kernelMatrix class to matrix objects
Support Vector Machines
Probabilities Coupling function
Class "csi"
Cholesky decomposition with Side Information
Kernel Functions
Class "gausspr"
Gaussian processes for regression and classification
Class "inchol"
Incomplete Cholesky decomposition
Onlearn object initialization
Class "ipop"
Quadratic Programming Solver
Class "kcca"
Kernel Canonical Correlation Analysis
Class "kernel" "rbfkernel" "polykernel", "tanhkernel", "vanillakernel"
Kernel Matrix functions
Class "kfa"
Kernel Feature Analysis
Class "kha"
Kernel Principal Components Analysis
Kernel k-means
Class "lssvm"
Least Squares Support Vector Machine
Class "kqr"
Kernel Maximum Mean Discrepancy.
Class "kpca"
Kernel Principal Components Analysis
Class "kqr"
Kernel Quantile Regression.
Class "ksvm"
Kernel Online Learning algorithms
plot method for support vector object
Class "prc"
predict method for Gaussian Processes object
Predict method for kernel Quantile Regression object
predict method for support vector object
Class "ranking"
Ranking
Class "rvm"
Relevance Vector Machine
Hyperparameter estimation for the Gaussian Radial Basis kernel
Class "specc"
Spectral Clustering
String Kernel Functions
Class "vm"
Kernel-based machine learning methods for classification, regression, clustering, novelty detection, quantile regression and dimensionality reduction. Among other methods 'kernlab' includes Support Vector Machines, Spectral Clustering, Kernel PCA, Gaussian Processes and a QP solver.