Description Usage Arguments Details Value Fields Author(s) References Examples
sslLDS
implements low density separation with Transductive Support Vector Machines(TSVM) for semi-supervised binary classification
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xl |
a n * p matrix or data.frame of labeled data |
yl |
a n * 1 binary labels(1 or -1). |
xu |
a m * p matrix or data.frame of unlabeled data. |
rho |
numeric;a parameter for connectivity kernel.It defines minimal rho-path distances. |
C |
numeric; a parameter in the TSVM training model. |
dist.type |
character string; this parameter controls the type of distance measurement.(see |
p |
the percentage of data used for cross-validation set. |
improvement |
numeric; minimal allowed improvement of parameters. |
seed |
an integer specifying random number generation state for spliting labeled data into training set and cross-validation set. |
delta |
numeric; a allowed cutoff for the cumulative percent of variance to lose by multidimensional scaling. |
alpha |
numeric; a learning rate in the gradient descent algorithm. |
sslLDS
constructs a low density graph with connectivity kernel.It implements multidemensional scaling
for demensionality reduction and chooses optimal C.star
by cross-validation. Finally, it trains the TSVM model with gradient descent algorithm.
a list of values is returned:
yu
the predicted label of unlabeled data
optC.star
the optimal C.star chosen by cross-validation. C.star weights the unlabeled data in the TSVM model.
para
estimated parameters of TSVM, including w
and b
Junxiang Wang
Chapelle, O., & Zien, A. (2005) Semi-supervised classification by low density separation.In Proceedings of the tenth international workshop on artificial intelligence and statistics.(pp. 57-64). Barbados.
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