Bimodal distribution removal

@incollection{citeulike:2872857, abstract = {A number of methods for cleaning up noisy training sets to improve generalisation have been proposed recently. Most of these methods perform well on artificially noisy data, but less well on real world data where it is difficult to distinguish between noisy data points from valid but rare data points.}, author = {Slade, P. and Gedeon, T. }, citeulike-article-id = {2872857}, doi = {http://dx.doi.org/10.1007/3-540-56798-4\_155}, journal = {New Trends in Neural Computation}, pages = {249--254}, posted-at = {2008-06-07 21:24:08}, priority = {5}, title = {Bimodal distribution removal}, url = {http://dx.doi.org/10.1007/3-540-56798-4\_155}, year = {1993} }

TY - CHAP ID - citeulike:2872857 L3 - citeulike-article-id:2872857 TI - Bimodal distribution removal JF - New Trends in Neural Computation SP - 249 EP - 254 N2 - A number of methods for cleaning up noisy training sets to improve generalisation have been proposed recently. Most of these methods perform well on artificially noisy data, but less well on real world data where it is difficult to distinguish between noisy data points from valid but rare data points. AU - Slade, P AU - Gedeon, T PY - 1993/// UR - http://dx.doi.org/10.1007/3-540-56798-4_155 ER -