SAM-package

The package "SAM" targets at high dimensional classification problem for complex data analysis. SAM is short for "Sparse Additive Machine", which is a explicit combination of high dimensional sparse additive modeling and support vector machine. Different from existing non-linear classification methods, which usually use , SAM adopts the computationally efficient basis spline technique. The optimization is solved by the Linearized Alternative Direction Method of Multipliers (L-ADMM). The computation is further accelerated by "warm-start" and "active-set" tricks. For users who are interested in large-scale problems, we also provide an implementation of L1 norm SVM for computational convenience.

The package SAM targets at high dimensional classification
        problem for complex data analysis. SAM is short for sparse
        additive machine, which is a explicit combination of high
        dimensional sparse additive modeling and support vector
        machine. Different from existing non-linear classification
        methods, which usually use, SAM adopts the computationally
        efficient basis spline technique. The optimization is solved by
        the Linearized Alternative Direction Method of Multipliers
        (L-ADMM). The computation is further accelerated by warm-start
        and active-set tricks. For users who are interested in
        large-scale problems, we also provide an implementation of L1
        norm SVM for computational convenience.

SAM-package: Sparse Additive Machine

Description

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