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- CoBoosting abstract "CoBoost is a semi-supervised training algorithm proposed by Collins and Singer in 1999. The original application for the algorithm was the task of Named Entity Classification using very weak learners. It can be used for performing semi-supervised learning in cases in which there exist redundancy in features.It may be seen as a combination of co-training and boosting. Each example is available in two views (subsections of the feature set), and boosting is applied iteratively in alternation with each view using predicted labels produced in the alternate view on the previous iteration. CoBoosting is not a valid boosting algorithm in the PAC learning sense.".
- CoBoosting wikiPageID "23642426".
- CoBoosting wikiPageRevisionID "577874892".
- CoBoosting hasPhotoCollection CoBoosting.
- CoBoosting subject Category:Classification_algorithms.
- CoBoosting type Abstraction100002137.
- CoBoosting type Act100030358.
- CoBoosting type Activity100407535.
- CoBoosting type Algorithm105847438.
- CoBoosting type ClassificationAlgorithms.
- CoBoosting type Event100029378.
- CoBoosting type Procedure101023820.
- CoBoosting type PsychologicalFeature100023100.
- CoBoosting type Rule105846932.
- CoBoosting type YagoPermanentlyLocatedEntity.
- CoBoosting comment "CoBoost is a semi-supervised training algorithm proposed by Collins and Singer in 1999. The original application for the algorithm was the task of Named Entity Classification using very weak learners. It can be used for performing semi-supervised learning in cases in which there exist redundancy in features.It may be seen as a combination of co-training and boosting.".
- CoBoosting label "CoBoosting".
- CoBoosting sameAs m.06znwf2.
- CoBoosting sameAs Q5137519.
- CoBoosting sameAs Q5137519.
- CoBoosting sameAs CoBoosting.
- CoBoosting wasDerivedFrom CoBoosting?oldid=577874892.
- CoBoosting isPrimaryTopicOf CoBoosting.