Description Usage Arguments Details Value References
Democratic is a semi-supervised learning algorithm with a co-training
style. This algorithm trains N classifiers with different learning schemes defined in
list gen.learners
. During the iterative process, the multiple classifiers with
different inductive biases label data for each other.
1 | democraticG(y, gen.learners, gen.preds)
|
y |
A vector with the labels of training instances. In this vector the
unlabeled instances are specified with the value |
gen.learners |
A list of functions for training N different supervised base classifiers. Each function needs two parameters, indexes and cls, where indexes indicates the instances to use and cls specifies the classes of those instances. |
gen.preds |
A list of functions for predicting the probabilities per classes.
Each function must be two parameters, model and indexes, where the model
is a classifier trained with |
democraticG can be helpful in those cases where the method selected as
base classifier needs a learner
and pred
functions with other
specifications. For more information about the general democratic method,
please see democratic
function. Essentially, democratic
function is a wrapper of democraticG
function.
A list object of class "democraticG" containing:
A vector with the confidence-weighted vote assigned to each classifier.
A list with the final N base classifiers trained using the enlarged labeled set.
List of N vectors of indexes related to the training instances
used per each classifier. These indexes are relative to the y
argument.
The indexes of all training instances used to
train the N models
. These indexes include the initial labeled instances
and the newly labeled instances. These indexes are relative to the y
argument.
List of three vectors with the same information in model.index
but the indexes are relative to instances.index
vector.
The levels of y
factor.
Yan Zhou and Sally Goldman.
Democratic co-learning.
In IEEE 16th International Conference on Tools with Artificial Intelligence (ICTAI),
pages 594-602. IEEE, Nov 2004. doi: 10.1109/ICTAI.2004.48.
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