Package: SAM
Type: Package
Title: Sparse Additive Machine
Version: 1.0.2
Date: 2012-08-19
Author: Tuo Zhao, Xingguo Li, Han Liu, Lie Wang, Kathryn Roeder
Maintainer: Tuo Zhao <tourzhao@gmail.com>
Depends: R (>= 2.10), splines
Description: 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.
License: GPL-2
Packaged: 2012-10-15 16:08:58 UTC; tourzhao
Repository: CRAN
Date/Publication: 2012-10-15 16:25:19

Author: Tuo Zhao, Xingguo Li, Han Liu, and Kathryn Roeder
Description: The package SAM targets at high dimensional predictive
modeling (regression and classification) for complex data
analysis. SAM is short for sparse additive modeling, and adopts
the computationally efficient basis spline technique. We solve
the optimization problems by various computational algorithms
including the block coordinate descent algorithm, fast
iterative soft-thresholding algorithm, and newton method. The
computation is further accelerated by warm-start and active-set

Author: Haoming Jiang, Yukun Ma, Xinyu Fei, Tuo Zhao, Xingguo Li, Han Liu, and Kathryn Roeder
Description: Computationally efficient tools for high dimensional predictive
modeling (regression and classification). SAM is short for sparse
additive modeling, and adopts the computationally efficient basis
spline technique. We solve  the optimization problems by various
computational algorithms including the block coordinate descent
algorithm, fast iterative soft-thresholding algorithm, and newton method.
The computation is further accelerated by warm-start and active-set tricks.