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Research Track Paper On the Use of Linear Programming for Unsupervised Text Classification

Authors: Mark Sandler The Pennsylvania State University CiteSeerX Archives

Contributors: Mark Sandler The Pennsylvania State University CiteSeerX Archives

Source: http://www.cs.uiuc.edu/class/fa05/cs591han/kdd05/docs/p256.pdf.

Subject Terms: Categories and Subject Descriptors H.3.3 [Information Storage and Retrieval, Clustering, Information Filtering, F.2.2 [Analysis of Algorithms and Problem Complexity, Non-numerical Algorithms and Problems, H.1.2 [Information Systems, Models and PrinciplesUser/Machine systemsHuman information processing General Terms Algorithms, theory, experimentation. Keywords Dimensionality reduction, generative models, mixture models, unsupervised learning, L1 norm, latent class models, linear programming

File Description: application/pdf

Relation: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.386.7510; http://www.cs.uiuc.edu/class/fa05/cs591han/kdd05/docs/p256.pdf

Availability: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.386.7510
http://www.cs.uiuc.edu/class/fa05/cs591han/kdd05/docs/p256.pdf