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SemiSupervised (version 1.0)

Safe Semi-Supervised Learning Tools

Description

Implements several safe graph-based semi-supervised learning algorithms. The first algorithm is the Semi-Supervised Semi-Parametric Model (S4PM) and the fast Anchor Graph version of this approach. For additional technical details, refer to Culp and Ryan (2013) , Ryan and Culp (2015) and the package vignette. The underlying fitting routines are executed in C++. All tuning parameter estimation is optimized using K-fold Cross-Validation.

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Version

Install

install.packages('SemiSupervised')

Monthly Downloads

8

Version

1.0

License

GPL-2

Maintainer

Mark Culp

Last Published

May 11th, 2018

Functions in SemiSupervised (1.0)

jtharm-class

Class ‘jtharm’
AnchorGraph

Generate an Anchor Graph from an n x p data matrix
SemiSupervised.control

Control Parameters for the S4-generic generic functions derived from virtual class SemiSupervised
jtharm

Joint Harmonic Functions (jtharm)
agraph-class

Class ‘agraph’
dG

Specify graph terms for ‘formula’ instances of objects contained in “SemiSupervised”
impute.median

Median imputation for NA's'
s4pm-class

Class ‘s4pm’
agraph

Anchor Graph Functions (agraph)
SemiSupervised-class

Class ‘SemiSupervised’
cv.folds

Exported Internal Functions
x.scaleL

Scale a data set in accordance to a labeled index set
predict.s4pm

Out-of-Sample Predict Procedure for s4pm
knnGraph

Convert a ‘data.frame’ or ‘matrix’ into a k-NN graph or epsilon graph.
s4pm

Safe Semi-Supervised Semi-Parametric Model (s4pm)
predict.jtharm

Out-of-Sample Predict Procedure for jtharm
predict.agraph

Out-of-Sample Predict Procedure for agraph
PowerPlant

The Combined Cycle Power Plant Data Set