Choosing Between Two Learning Algorithms Export

@misc{citeulike:247904, abstract = {Designing a hypothesis test to determine the best of two machine learning algorithms with only a small data set available is not a simple task. Many popular tests su\#er from low power (5x2 cv [2]), or high Type I error (Weka's 10x10 cross validation [11]). Furthermore, many tests show a low level of replicability, so that tests performed by different scientists with the same pair of algorithms, the same data sets and the same hypothesis test still may present di\#erent results.}, author = {Calibrated, Based O.}, citeulike-article-id = {247904}, citeulike-linkout-0 = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.58.4949}, keywords = {replicability}, posted-at = {2005-07-06 16:54:52}, priority = {2}, title = {Choosing Between Two Learning Algorithms}, url = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.58.4949} }

TY - GEN ID - citeulike:247904 L3 - citeulike-article-id:247904 N2 - Designing a hypothesis test to determine the best of two machine learning algorithms with only a small data set available is not a simple task. Many popular tests su#er from low power (5x2 cv [2]), or high Type I error (Weka's 10x10 cross validation [11]). Furthermore, many tests show a low level of replicability, so that tests performed by different scientists with the same pair of algorithms, the same data sets and the same hypothesis test still may present di#erent results. TI - Choosing Between Two Learning Algorithms KW - replicability AU - Calibrated, Based UR - http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.58.4949 ER -