bst v0.3-17

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Gradient Boosting

Functional gradient descent algorithm for a variety of convex and non-convex loss functions, for both classical and robust regression and classification problems. See Wang (2011) <doi:10.2202/1557-4679.1304>, Wang (2012) <doi:10.3414/ME11-02-0020>, Wang (2018) <doi:10.1080/10618600.2018.1424635>, Wang (2018) <doi:10.1214/18-EJS1404>.

Functions in bst

Name Description
cv.mada Cross-Validation for one-vs-all AdaBoost with multi-class problem
cv.mbst Cross-Validation for Multi-class Boosting
cv.rbst Cross-Validation for Nonconvex Loss Boosting
cv.rmbst Cross-Validation for Nonconvex Multi-class Loss Boosting
bfunc Compute upper bound of second derivative of loss
loss Internal Function
bst Boosting for Classification and Regression
bst.sel Function to select number of predictors
nsel Find Number of Variables In Multi-class Boosting Iterations
rbst Robust Boosting for Robust Loss Functions
mada Multi-class AdaBoost
bst_control Control Parameters for Boosting
mbst Boosting for Multi-Classification
cv.bst Cross-Validation for Boosting
rbstpath Robust Boosting Path for Nonconvex Loss Functions
evalerr Compute prediction errors
ex1data Generating Three-class Data with 50 Predictors
rmbst Robust Boosting for Multi-class Robust Loss Functions
cv.mhingebst Cross-Validation for Multi-class Hinge Boosting
cv.mhingeova Cross-Validation for one-vs-all HingeBoost with multi-class problem
mhingebst Boosting for Multi-class Classification
mhingeova Multi-class HingeBoost
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Vignettes of bst

Name
bst.bib
cancer.Rnw
khan.Rnw
mcl.Rnw
pros.Rnw
static_cancer.pdf.asis
static_khan.pdf.asis
static_mcl.pdf.asis
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Details

Type Package
Date 2019-01-31
VignetteBuilder R.rsp, knitr
License GPL (>= 2)
LazyLoad yes
Packaged 2019-02-27 15:15:38 UTC; zhu
NeedsCompilation no
Repository CRAN
Date/Publication 2019-02-27 17:00:25 UTC

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