%0 Conference Paper %1 FT004 %A Lewis, D. D. %A Ringuette, M. %B Symposium on Document Analysis and Information Retrieval. %D 1994 %K DT-min10. Inductive PropBayes, bayesian classifier, decision methods, trees, %T A comparison of two learning algorithms for text categorization.

@inproceedings{FT004, added-at = {2009-06-22T10:05:09.000+0200}, author = {Lewis, D. D. and Ringuette, M.}, biburl = {https://www.bibsonomy.org/bibtex/2efdd3d5402ec4dcce2e2ae7e5b979011/n.nanas}, booktitle = {Symposium on Document Analysis and Information Retrieval.}, interhash = {16cf6dbf8cfca1132333e7852aecc31f}, intrahash = {efdd3d5402ec4dcce2e2ae7e5b979011}, keywords = {DT-min10. Inductive PropBayes, bayesian classifier, decision methods, trees,}, qnote = {Not a very useful paper. The only interesting point is the observation that preliminary feature selection helps in improving the systems performance. They are using information gain which however can not be used in my case since it is also using negative documents.}, timestamp = {2009-06-23T10:19:15.000+0200}, title = {A comparison of two learning algorithms for text categorization.}, year = 1994 }