SVMlin is software package for linear SVMs. It is well-suited to
classification problems involving a large number of examples and features.
It is primarily written for sparse datasets.
SVMlin can also utilize unlabeled data, in addition to labeled examples.
It currently implements two extensions of standard SVMs to incorporate
unlabeled examples.
SVMlin implements the following algorithms:
- Fully supervised [using only labeled examples]
* Linear Regularized Least Squares (RLS) Classification
* Modified Finite Newton Linear L2-SVMs
- Semi-supervised [can use unlabeled data as well]
* Linear Transductive L2-SVMs with multiple switchings
* Deterministic Annealing (DA) for Semi-supervised Linear L2-SVMs