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+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