This unit implements several Suport-Vector Machine (SVM)
machine learning models on the tangent space for symmetric positive definite
(SDP) matrices, i.e., real PD matrices.
Several models can be obtained with different combinations of the svmType and the kernel arguments when the model is fit.
Optimal hyperparameters for the given training data
are found using cross-validation.
All SVM models are implemented using the Julia LIBSVM.jl package. See 🎓 for resources on the original LIBSVM C library and learn how to use purposefully these models.
The fit, predict and cvAcc functions for the SVM models are
reported in the cv.jl unit, since those are homogeneous across all
machine learning models. Here it is reported the SVMmodel
abstract type and the SVM structure.
SVMmodel
SVM