CoBoosting
Encyclopedia
CoBoost is a variant of Boosting
proposed by Collins and Singer.
It may be seen as a combination of co-training
and boosting
. Each example is available in two views, and boosting
is applied iteratively and alternatively in each views using pseudo-labels produced in the other view.
Boosting
Boosting is a machine learning meta-algorithm for performing supervised learning. Boosting is based on the question posed by Kearns: can a set of weak learners create a single strong learner? A weak learner is defined to be a classifier which is only slightly correlated with the true classification...
proposed by Collins and Singer.
It may be seen as a combination of co-training
Co-training
Co-training is a machine learning algorithm used when there are only small amounts of labeled data and large amounts of unlabeled data. One of its uses is in text mining for search engines. It was introduced by Avrim Blum and Tom Mitchell in 1998....
and boosting
Boosting
Boosting is a machine learning meta-algorithm for performing supervised learning. Boosting is based on the question posed by Kearns: can a set of weak learners create a single strong learner? A weak learner is defined to be a classifier which is only slightly correlated with the true classification...
. Each example is available in two views, and boosting
Boosting
Boosting is a machine learning meta-algorithm for performing supervised learning. Boosting is based on the question posed by Kearns: can a set of weak learners create a single strong learner? A weak learner is defined to be a classifier which is only slightly correlated with the true classification...
is applied iteratively and alternatively in each views using pseudo-labels produced in the other view.