Co-training - Revision history

Koedinger at 16:40, 20 August 2007

2007-08-20T16:40:14Z

← Older revision		Revision as of 16:40, 20 August 2007
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	[[Category:Glossary]]		[[Category:Glossary]]
	[[Category:PSLC General]]		[[Category:PSLC General]]
			[[Category:Learning Processes]]
	[[Category:Coordinative Learning]]		[[Category:Coordinative Learning]]

Koedinger at 16:42, 28 December 2006

2006-12-28T16:42:33Z

← Older revision		Revision as of 16:42, 28 December 2006
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	A [[self-supervised learning]] method for learning from multiple sources. Co-training may be effective because the two or more [[sources]] of information may improve learning, particularly when there is a mix of unlabeled source examples with labeled examples.		A [[self-supervised learning]] method for learning from multiple sources. Co-training may be effective because the two or more [[sources]] of information may improve learning, particularly when there is a mix of unlabeled source examples with labeled examples.

	~~This~~ may be related to the testing effect, since the multiple sources of unlabeled information may improve the generation of labels that occurs in learning. Multiple sources may provide additional learning because they provide a broader basis from which to generate a label (meaning).		The learning benefits of co-training may be related to the testing effect, since the multiple sources of unlabeled information may improve the generation of labels that occurs in learning. Multiple sources may provide additional learning because they provide a broader basis from which to generate a label (meaning).

	A.Blum and T. Mitchell. Combining labeled and unlabeled data with co-training. In Proceedings of the 1998 Conference on Computational Learning Theory, July 1998.		A.Blum and T. Mitchell. Combining labeled and unlabeled data with co-training. In Proceedings of the 1998 Conference on Computational Learning Theory, July 1998.

Adele at 15:21, 27 December 2006

2006-12-27T15:21:06Z

← Older revision		Revision as of 15:21, 27 December 2006
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	A [[self-supervised learning]] method for learning from multiple sources. Co-training may be effective because the two or more [[sources]] of information may improve learning, particularly when there is a mix of [[unlabeled]] source examples with labeled examples.		A [[self-supervised learning]] method for learning from multiple sources. Co-training may be effective because the two or more [[sources]] of information may improve learning, particularly when there is a mix of unlabeled source examples with labeled examples.

	This may be related to the testing effect, since the multiple sources of unlabeled information may improve the generation of labels that occurs in learning. Multiple sources may provide additional learning because they provide a broader basis from which to generate a label (meaning).		This may be related to the testing effect, since the multiple sources of unlabeled information may improve the generation of labels that occurs in learning. Multiple sources may provide additional learning because they provide a broader basis from which to generate a label (meaning).

Adele at 15:20, 27 December 2006

2006-12-27T15:20:39Z

← Older revision		Revision as of 15:20, 27 December 2006
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	A [[self-supervised learning]] method for learning from multiple sources. Co-training may be effective because the two or more [[sources]] of information may improve learning, particularly when there is a mix of unlabeled source examples with labeled examples.		A [[self-supervised learning]] method for learning from multiple sources. Co-training may be effective because the two or more [[sources]] of information may improve learning, particularly when there is a mix of [[unlabeled]] source examples with labeled examples.

	This may be related to the testing effect, since the multiple sources of unlabeled information may improve the generation of labels that occurs in learning. Multiple sources may provide additional learning because they provide a broader basis from which to generate a label (meaning).		This may be related to the testing effect, since the multiple sources of unlabeled information may improve the generation of labels that occurs in learning. Multiple sources may provide additional learning because they provide a broader basis from which to generate a label (meaning).

Adele at 14:53, 27 December 2006

2006-12-27T14:53:46Z

← Older revision		Revision as of 14:53, 27 December 2006
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	A [[self-supervised learning]] method for learning from multiple sources. Co-training may be effective because the two or more sources of information may improve learning, particularly when there is a mix of unlabeled source examples with labeled examples.		A [[self-supervised learning]] method for learning from multiple sources. Co-training may be effective because the two or more [[sources]] of information may improve learning, particularly when there is a mix of unlabeled source examples with labeled examples.

	This may be related to the testing effect, since the multiple sources of unlabeled information may improve the generation of labels that occurs in learning. Multiple sources may provide additional learning because they provide a broader basis from which to generate a label (meaning).		This may be related to the testing effect, since the multiple sources of unlabeled information may improve the generation of labels that occurs in learning. Multiple sources may provide additional learning because they provide a broader basis from which to generate a label (meaning).

Adele at 20:25, 21 December 2006

2006-12-21T20:25:10Z

← Older revision		Revision as of 20:25, 21 December 2006
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	A self-supervised learning method for learning from multiple sources. Co-training may be effective because the two or more sources of information may improve learning, particularly when there is a mix of unlabeled source examples with labeled examples.		A [[self-supervised learning]] method for learning from multiple sources. Co-training may be effective because the two or more sources of information may improve learning, particularly when there is a mix of unlabeled source examples with labeled examples.

	This may be related to the testing effect, since the multiple sources of unlabeled information may improve the generation of labels that occurs in learning. Multiple sources may provide additional learning because they provide a broader basis from which to generate a label (meaning).		This may be related to the testing effect, since the multiple sources of unlabeled information may improve the generation of labels that occurs in learning. Multiple sources may provide additional learning because they provide a broader basis from which to generate a label (meaning).

PhilPavlik at 18:30, 27 November 2006

2006-11-27T18:30:45Z

← Older revision		Revision as of 18:30, 27 November 2006
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	A self-supervised learning method for learning from unlabeled examples ~~and~~ multiple sources. ~~See papers by~~ Blum and Mitchell.		A self-supervised learning method for learning from multiple sources. Co-training may be effective because the two or more sources of information may improve learning, particularly when there is a mix of unlabeled source examples with labeled examples.

			This may be related to the testing effect, since the multiple sources of unlabeled information may improve the generation of labels that occurs in learning. Multiple sources may provide additional learning because they provide a broader basis from which to generate a label (meaning).

			A.Blum and T. Mitchell. Combining labeled and unlabeled data with co-training. In Proceedings of the 1998 Conference on Computational Learning Theory, July 1998.

	[[Category:Glossary]]		[[Category:Glossary]]
	[[Category:PSLC General]]		[[Category:PSLC General]]
	[[Category:Coordinative Learning]]		[[Category:Coordinative Learning]]

PhilPavlik at 17:36, 27 November 2006

2006-11-27T17:36:44Z

← Older revision		Revision as of 17:36, 27 November 2006
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	A self-supervised learning method for learning from unlabeled examples and multiple sources. See papers by Blum and Mitchell.		A self-supervised learning method for learning from unlabeled examples and multiple sources. See papers by Blum and Mitchell.
			[[Category:Glossary]]
			[[Category:PSLC General]]
			[[Category:Coordinative Learning]]

Jodi-Davenport at 17:27, 27 November 2006

2006-11-27T17:27:59Z

New page

A self-supervised learning method for learning from unlabeled examples and multiple sources. See papers by Blum and Mitchell.