%0 Conference Paper %1 FT002 %A Ng, H. T. %A Goh, W. B. %A Low, K. L. %B 20th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. %C New York %D 1997 %K Feature algorithms, learning learning. perceptron selection, %P 67-73 %T Feature selection, perception learning, and a usability case study for text categorization.

@inproceedings{FT002, added-at = {2009-06-22T10:05:09.000+0200}, address = {New York}, author = {Ng, H. T. and Goh, W. B. and Low, K. L.}, biburl = {https://www.bibsonomy.org/bibtex/2a3c715fd9652f5f8b02d09bcd7ce82a0/n.nanas}, booktitle = {20th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval.}, interhash = {8c39b4fc18bd43365c5ff8cb5896ea80}, intrahash = {a3c715fd9652f5f8b02d09bcd7ce82a0}, keywords = {Feature algorithms, learning learning. perceptron selection,}, pages = {67-73}, qnote = {Not a rich paper. The new feature selection formula is nothing impressive. They are using negative examples as well in most of the cases. The use of only possitively weighted terms results in better performance. The most interesting part of the paper is the use of perceptron learning algorithm which could prove usefule in my case.}, timestamp = {2009-06-23T10:19:15.000+0200}, title = {Feature selection, perception learning, and a usability case study for text categorization.}, year = 1997 }