coBCReg is based on an ensemble of N diverse regressors. At each iteration and for each regressor, the companion committee labels the unlabeled examples then the regressor select the most informative newly-labeled examples for itself, where the selection confidence is based on estimating the validation error. The final prediction is the average of the estimates of the N regressors.
coBCRegG(
y,
gen.learner,
gen.pred,
N = 3,
perc.full = 0.7,
u = 100,
max.iter = 50,
gr = 1
)A vector with the labels of training instances. In this vector the
unlabeled instances are specified with the value NA.
A function for training N supervised base classifiers.
This function needs two parameters, indexes and cls, where indexes indicates
the instances to use and cls specifies the classes of those instances.
A function for predicting the probabilities per classes.
This function must be two parameters, model and indexes, where the model
is a classifier trained with gen.learner function and
indexes indicates the instances to predict.
The number of classifiers used as committee members. All these classifiers
are trained using the gen.learner function. Default is 3.
A number between 0 and 1. If the percentage of new labeled examples reaches this value the self-labeling process is stopped. Default is 0.7.
Number of unlabeled instances in the pool. Default is 100.
Maximum number of iterations to execute in the self-labeling process. Default is 50.
growing rate
For regression tasks, labeling data is very expensive computationally. Its so slow.
Mohamed Farouk Abdel-Hady, Mohamed Farouk Abdel-Hady and G<U+00FC>nther Palm. Semi-supervised Learning for Regression with Cotraining by Committee Institute of Neural Information Processing University of Ulm D-89069 Ulm, Germany