Bolstered Error Estimation

@misc{citeulike:1198013, abstract = {We propose agT\"kxd methodfor error estimation that displays low varianceandgcedIIIT low bias as well. This method is based on \"bolstering theorig\#G\" empirical distribution of the data. It has a directgrectdIk interpretation and can be easily appliedto any classi\#cation rule andany number of classes. This methodcan be usedto improve the performance of any error-counting estimation method, such as resubstitution and all cross-validation estimators, particularly in small-sample settings We ...}, author = {Neto, Ulisses B. and Dougherty, Edward }, citeulike-article-id = {1198013}, keywords = {cross-validation, machine-learning}, posted-at = {2007-03-30 14:53:27}, priority = {5}, title = {Bolstered Error Estimation}, url = {http://citeseer.ist.psu.edu/710774.html} }

TY - GEN ID - citeulike:1198013 L3 - citeulike-article-id:1198013 TI - Bolstered Error Estimation N2 - We propose agT"kxd methodfor error estimation that displays low varianceandgcedIIIT low bias as well. This method is based on "bolstering theorig#G" empirical distribution of the data. It has a directgrectdIk interpretation and can be easily appliedto any classi#cation rule andany number of classes. This methodcan be usedto improve the performance of any error-counting estimation method, such as resubstitution and all cross-validation estimators, particularly in small-sample settings We ... KW - cross-validation KW - machine-learning AU - Neto, Ulisses AU - Dougherty, Edward UR - http://citeseer.ist.psu.edu/710774.html ER -