Partially Supervised Classification of Text Documents

Keywords

Computer science, Class (philosophy), Set (abstract data type), Feature (linguistics), Supervised learning

References

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• A comparison of event models for naive bayes text classificationAndrew McCallum, Kamal Nigam — 1998
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• Text Categorization with Support Vector Machines: Learning with Many Relevant FeaturesThorsten Joachims — 1999
• Efficient noise-tolerant learning from statistical queriesMichael Kearns — 1998
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• Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paperA. P. Dempster, N. M. Laird, Donald B. Rubin — 1977
• Developments in Automatic Text RetrievalGerard Salton — 1991
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• Decision Theoretic Generalizations of the PAC Model for Neural Net and Other Learning ApplicationsDavid Haussler — 1992
• An Evaluation of Statistical Approaches to Text CategorizationYiming Yang — 1999
• Learning to Classify Text from Labeled and Unlabeled DocumentsKamal Nigam, Andrew McCallum, Sebastian Thrun — 1998
• Relevance feedback in information retrievalJ. J. Rocchio — 1971
• A comparison of two learning algorithms for text categorizationD. Lewis, M. Ringuette — 1994
• On the foundation of evaluationW. M. Shaw — 1986
• NewsWeeder: Learning to Filter NetnewsK. Lang — 1995
• PAC Learning from Positive Statistical QueriesFrançois Denis — 1998
• Learning From Positive and Unlabeled Examples ?Fabien Letouzey, François Denis, R. Emi — 2000
• Probability inequalities for sum of bounded random variablesW. Hoeffding — 1963

Cited by

• Positive Unlabeled Learning for Data Stream ClassificationXiaoli Li, Philip S. Yu, Bing Liu — 2009
• Learning to Classify Texts Using Positive and Unlabeled DataXiaoli Li, Bing Liu — 2003
• CFUI: Collaborative Filtering with Unlabeled ItemsJing Peng, Daniel Zeng, Bing Liu — 2010
• Personalised ontology learning and mining for web information gatheringXiaohui Tao — 2009
• TPN 2 : Using positive-only learning to deal with the heterogeneity of labeled and unlabeled dataNikolaos Trogkanis, Γεώργιος Παλιούρας — 2006
• Corpus Based Unsupervised Labeling of DocumentsDelip Rao, P Deepak, Deepak Khemani — 2006
• Reasoning with Lines in the Euclidean SpaceKhalil Challita — 2009
• Robust content-based image retrieval of multi-example queriesJun Zhang — 2011
• Identifying Protein Interaction Abstracts with Contextual Bag of WordsHsi-Chuan Hung, Richard Tzong‐Han Tsai, Wen−Lian Hsu — 2007
• Confidence Estimation Methods for Partially Supervised Information ExtractionEugene Agichtein — 2006
• Partially Supervised Text Classification with Multi-Level ExamplesTao Liu, Xiaoyong Du, Yongdong Xu — 2011
• Using PU-Learning to Detect Deceptive Opinion SpamDonato Hernández, Rafael Guzmán-Cabrera, Manuel Montes y Gómez — 2013
• Instance Selection and Instance Weighting for Cross-Domain Sentiment Classification via PU LearningRui Xia, Xuelei Hu, Jianfeng Lu — 2013
• Semi-Supervised Text Classification Using Positive and Unlabeled DataShuang Yu, Xueyuan Zhou, Chunping Li — 2006
• Extracting relations from large text collectionsLuis Gravano, Yevgeny Agichtein — 2005
• Learning to Extract Entities from Labeled and Unlabeled TextRosie Jones — 2005
• Text Classification by Labeling WordsBing Liu, Xiaoli Li, Wee Sun Lee — 2004
• Learning from positive and unlabeled examples in biology. (Méthodes d'apprentissage statistique à partir d'exemples positifs et indéterminés en biologie)Fantine Mordelet — 2010
• Applying CBR Principles to Reason without Negative ExemplarsSidath Gunawardena, Rosina O. Weber — 2013
• Understanding and exploiting user intent in community question answeringLong Chen — 2014

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