Tri - training algorithm - diverse classifiers The 2019 Stack Overflow Developer Survey Results Are Inhow to build a predictive model without training data neither historical data
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Tri - training algorithm - diverse classifiers
The 2019 Stack Overflow Developer Survey Results Are Inhow to build a predictive model without training data neither historical data
$begingroup$
I am studying a tri training algorithm proposed by this aticle.
On page 3, it stated:
"It is noteworthy that the initial classifiers in tri-training should
be diverse because if all the classifiers are identical, then for any
of these classifiers, the unlabeled examples labeled by the other two
classifiers will be the same as these labeled by the classifier for
itself. Thus, tri-training degenerates to self-training [19] with a
single classifier."
I would like to ask, what does it mean? Is it sufficient that all initial classifiers are trained on different datasets, or it is necessary to use different models (e.g. KNN, decision tree, SVM)?
Thanks.
semi-supervised-learning
edited Mar 29 at 21:04
Ethan
701625
asked Mar 29 at 19:03
SMI9SMI9
64
$endgroup$
add a comment |
$begingroup$
I am studying a tri training algorithm proposed by this aticle.
On page 3, it stated:
"It is noteworthy that the initial classifiers in tri-training should
be diverse because if all the classifiers are identical, then for any
of these classifiers, the unlabeled examples labeled by the other two
classifiers will be the same as these labeled by the classifier for
itself. Thus, tri-training degenerates to self-training [19] with a
single classifier."
I would like to ask, what does it mean? Is it sufficient that all initial classifiers are trained on different datasets, or it is necessary to use different models (e.g. KNN, decision tree, SVM)?
Thanks.
semi-supervised-learning
edited Mar 29 at 21:04
Ethan
701625
asked Mar 29 at 19:03
SMI9SMI9
64
$endgroup$
add a comment |
$begingroup$
I am studying a tri training algorithm proposed by this aticle.
On page 3, it stated:
"It is noteworthy that the initial classifiers in tri-training should
be diverse because if all the classifiers are identical, then for any
of these classifiers, the unlabeled examples labeled by the other two
classifiers will be the same as these labeled by the classifier for
itself. Thus, tri-training degenerates to self-training [19] with a
single classifier."
I would like to ask, what does it mean? Is it sufficient that all initial classifiers are trained on different datasets, or it is necessary to use different models (e.g. KNN, decision tree, SVM)?
Thanks.
semi-supervised-learning
edited Mar 29 at 21:04
Ethan
701625
asked Mar 29 at 19:03
SMI9SMI9
64
$endgroup$
I am studying a tri training algorithm proposed by this aticle.
On page 3, it stated:
"It is noteworthy that the initial classifiers in tri-training should
be diverse because if all the classifiers are identical, then for any
of these classifiers, the unlabeled examples labeled by the other two
classifiers will be the same as these labeled by the classifier for
itself. Thus, tri-training degenerates to self-training [19] with a
single classifier."
I would like to ask, what does it mean? Is it sufficient that all initial classifiers are trained on different datasets, or it is necessary to use different models (e.g. KNN, decision tree, SVM)?
Thanks.
semi-supervised-learning
semi-supervised-learning
edited Mar 29 at 21:04
Ethan
701625
asked Mar 29 at 19:03
SMI9SMI9
64
edited Mar 29 at 21:04
Ethan
701625
asked Mar 29 at 19:03
SMI9SMI9
64
edited Mar 29 at 21:04
Ethan
701625
edited Mar 29 at 21:04
Ethan
701625
edited Mar 29 at 21:04
Ethan
701625
701625
asked Mar 29 at 19:03
SMI9SMI9
64
asked Mar 29 at 19:03
SMI9SMI9
64
asked Mar 29 at 19:03
SMI9SMI9
64
64
add a comment |
add a comment |
0
active
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