%0 Journal Article %1 IF002 %A Losee, R. M. Jr. %D 1989 %J Journal on Information Science %K Information binary distribution. features, filtering information model, overload, poisson %N 3 %P 179-189 %T Minimizing Information Overload: the Ranking of Electronic Messages. %V 15

@article{IF002, added-at = {2009-06-22T10:05:09.000+0200}, author = {Losee, R. M. Jr.}, biburl = {https://www.bibsonomy.org/bibtex/25487fea7827ce9b454d76ecdfa875b2c/n.nanas}, date-modified = {2008-02-19 01:05:23 +0200}, interhash = {4e33f6aad6b3a1782ffcc53e6eba513a}, intrahash = {5487fea7827ce9b454d76ecdfa875b2c}, journal = {Journal on Information Science}, keywords = {Information binary distribution. features, filtering information model, overload, poisson}, number = 3, pages = {179-189}, qnote = {Very good paper. A good analytic model that could be used as a framework for the analysis of my approach. The paper includes a lot of usefull ideas and is also very rich in bibliogreaphy. Useful definitions of information overload. Argues that models that incorporate feature interdependence are less acurate due to inaccurate estimation of the increased involved parameters. My approach can over come this problem by combining both independency and dependency using two levels of relevance estimation. You can also see my meta features as virtual features organizing the features they are linked with at the lower level.}, read = {Yes}, timestamp = {2009-06-23T10:19:15.000+0200}, title = {Minimizing Information Overload: the Ranking of Electronic Messages.}, volume = 15, year = 1989 }