@article{zhu_semi-supervised_2008,
	title = {Semi-{Supervised} {Learning} {Literature} {Survey}},
	volume = {2},
	abstract = {We review the literature on semi-supervised learning, which is an area in machine learning and more generally, artificial intelligence. There has been a whole
spectrum of interesting ideas on how to learn from both labeled and unlabeled data, i.e. semi-supervised learning. This document is a chapter excerpt from the author’s
doctoral thesis (Zhu, 2005). However the author plans to update the online version frequently to incorporate the latest development in the field. Please obtain the latest
version at http://www.cs.wisc.edu/{\textasciitilde}jerryzhu/pub/ssl\_survey.pdf},
	journal = {Comput Sci, University of Wisconsin-Madison},
	author = {Zhu, Xiaojin},
	month = jul,
	year = {2008},
}

{"_id":{"_str":"534279630e946d920a001714"},"__v":1,"authorIDs":[],"author_short":["Zhu, X."],"bibbaseid":"zhu-semisupervisedlearningliteraturesurvey-2008","bibdata":{"bibtype":"article","type":"article","title":"Semi-Supervised Learning Literature Survey","volume":"2","abstract":"We review the literature on semi-supervised learning, which is an area in machine learning and more generally, artificial intelligence. There has been a whole spectrum of interesting ideas on how to learn from both labeled and unlabeled data, i.e. semi-supervised learning. This document is a chapter excerpt from the author’s doctoral thesis (Zhu, 2005). However the author plans to update the online version frequently to incorporate the latest development in the field. Please obtain the latest version at http://www.cs.wisc.edu/~jerryzhu/pub/ssl_survey.pdf","journal":"Comput Sci, University of Wisconsin-Madison","author":[{"propositions":[],"lastnames":["Zhu"],"firstnames":["Xiaojin"],"suffixes":[]}],"month":"July","year":"2008","bibtex":"@article{zhu_semi-supervised_2008,\n\ttitle = {Semi-{Supervised} {Learning} {Literature} {Survey}},\n\tvolume = {2},\n\tabstract = {We review the literature on semi-supervised learning, which is an area in machine learning and more generally, artificial intelligence. There has been a whole\nspectrum of interesting ideas on how to learn from both labeled and unlabeled data, i.e. semi-supervised learning. This document is a chapter excerpt from the author’s\ndoctoral thesis (Zhu, 2005). However the author plans to update the online version frequently to incorporate the latest development in the field. Please obtain the latest\nversion at http://www.cs.wisc.edu/{\\textasciitilde}jerryzhu/pub/ssl\\_survey.pdf},\n\tjournal = {Comput Sci, University of Wisconsin-Madison},\n\tauthor = {Zhu, Xiaojin},\n\tmonth = jul,\n\tyear = {2008},\n}\n\n\n\n","author_short":["Zhu, X."],"key":"zhu_semi-supervised_2008","id":"zhu_semi-supervised_2008","bibbaseid":"zhu-semisupervisedlearningliteraturesurvey-2008","role":"author","urls":{},"metadata":{"authorlinks":{}},"downloads":0,"html":""},"bibtype":"article","biburl":"https://bibbase.org/zotero/mh_lenguyen","downloads":0,"keywords":[],"search_terms":["semi","supervised","learning","literature","survey","zhu"],"title":"Semi-Supervised Learning Literature Survey","year":2008,"dataSources":["2cjnbEWxd35GBeqEe","iwKepCrWBps7ojhDx"]}