2000 AnalyzingTheEffectivAndApplOfCoTraining

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Subject Headings: Co-Training Algorithm.

Notes

Cited by

2006

2002

  • (Strehl & Ghosh, 2002b) ⇒ Alexander Strehl, and Joydeep Ghosh. (2002). “Cluster Ensembles -- A knowledge reuse framework for combining multiple partitions.” In: The Journal of Machine Learning Research, (3).

Quotes

Author Keywords

Abstract

Recently there has been significant interest in supervised learning algorithms that combine labeled and unlabeled data for text learning tasks. The co-training setting(Blum & Mitchell, 1998) applies to datasets that have a natural separation of their features into two disjoint sets. We demonstrate that when learning from labeled and unlabeled data, algorithms explicitly leveraging a natural independent split of the features outperform algorithms that do not. When a natural split does not exist, co-training algorithms that manufacture a feature split may out-perform algorithms not using a split. These results help explain why co-training algorithms are both discriminative in nature and robust to the assumptions of their embedded classifiers.

References


 AuthorvolumeDate ValuetitletypejournaltitleUrldoinoteyear
2000 AnalyzingTheEffectivAndApplOfCoTrainingKamal Nigam
Rayid Ghani
Analyzing the Effectiveness and Applicability of Co-traininghttp://www.cs.cmu.edu/~knigam/papers/cotrain-CIKM00.pdf10.1145/354756.354805