Partially Supervised Classification of Text Documents

by: B Liu, W Lee, P Yu, X Li

(2002)

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Haven't read it, but seems like semi-supervised novelty detection. We have some labeled examples from a Class 1, and lots of unlabeled examples, some of which are from Class 1.

davidr (public ) - 2005-03-29 21:01:29

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Abstract

We investigate the following problem: Given a set of documents of a particular topic or class # , and a large set # of mixed documents that contains documents from class # and other types of documents, identify the documents from class # in # . The key feature of this problem is that there is no labeled non- # document, which makes traditional machine learning techniques inapplicable, as they all need labeled documents of both classes. We call this problem partially supervised...

@misc{citeulike:142739, abstract = {We investigate the following problem: Given a set of documents of a particular topic or class \# , and a large set \# of mixed documents that contains documents from class \# and other types of documents, identify the documents from class \# in \# . The key feature of this problem is that there is no labeled non- \# document, which makes traditional machine learning techniques inapplicable, as they all need labeled documents of both classes. We call this problem partially supervised...}, author = {Liu, B. and Lee, W. and Yu, P. and Li, X. }, citeulike-article-id = {142739}, comment = {Haven't read it, but seems like semi-supervised novelty detection. We have some labeled examples from a Class 1, and lots of unlabeled examples, some of which are from Class 1. }, keywords = {partially-supervised, semisupervised}, posted-at = {2005-03-29 21:01:29}, priority = {2}, title = {Partially Supervised Classification of Text Documents}, url = {http://citeseer.ist.psu.edu/liu02partially.html}, year = {2002} }

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TY - GEN ID - citeulike:142739 L3 - citeulike-article-id:142739 TI - Partially Supervised Classification of Text Documents N2 - We investigate the following problem: Given a set of documents of a particular topic or class # , and a large set # of mixed documents that contains documents from class # and other types of documents, identify the documents from class # in # . The key feature of this problem is that there is no labeled non- # document, which makes traditional machine learning techniques inapplicable, as they all need labeled documents of both classes. We call this problem partially supervised... KW - partially-supervised KW - semisupervised N1 - Haven't read it, but seems like semi-supervised novelty detection. We have some labeled examples from a Class 1, and lots of unlabeled examples, some of which are from Class 1. AU - Liu, B AU - Lee, W AU - Yu, P AU - Li, X PY - 2002/// UR - http://citeseer.ist.psu.edu/liu02partially.html ER -

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