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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… Expand
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Papers overview

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Highly Cited
2011
Highly Cited
2011
We propose a spectral clustering algorithm for the multi-view setting where we have access to multiple views of the data, each of… Expand
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Highly Cited
2011
Highly Cited
2011
Domain adaptation algorithms seek to generalize a model trained in a source domain to a new target domain. In many practical… Expand
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Highly Cited
2009
Highly Cited
2009
The lack of Chinese sentiment corpora limits the research progress on Chinese sentiment classification. However, there are many… Expand
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Highly Cited
2007
Highly Cited
2007
Co-training is a semi-supervised learning paradigm which trains two learners respectively from two different views and lets the… Expand
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Highly Cited
2004
Highly Cited
2004
Co-training is a method for combining labeled and unlabeled data when examples can be thought of as containing two distinct sets… Expand
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Highly Cited
2004
Highly Cited
2004
This paper investigates the application of co-training and self-training to word sense disambiguation. Optimal and empirical… Expand
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Highly Cited
2001
Highly Cited
2001
The rule-based bootstrapping introduced by Yarowsky, and its co-training variant by Blum and Mitchell, have met with considerable… Expand
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Highly Cited
2001
Highly Cited
2001
The main problems in text classification are lack of labeled data, as well as the cost of labeling the unlabeled data. We address… Expand
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Highly Cited
2000
Highly Cited
2000
Recently there has been signi cant interest in supervised learning algorithms that combine labeled and unlabeled data for text… Expand
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Highly Cited
1998
Highly Cited
1998
We consider the problem of using a large unlabeled sample to boost performance of a learning algorit,hrn when only a small set of… Expand
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