A statistical classification algorithm and its application to language identification from noisy input are described. The main innovation is to compute confidence limits on the classification, so that the algorithm terminates when enough evidence to make a clear decision has been made, and so avoiding problems with categories that have similar characteristics. A second application, to genre ide...

A statistical classification algorithm and its application to language identification from noisy input are described. The main innovation is to compute confidence limits on the classification, so that the algorithm terminates when enough evidence to make a clear decision has been made, and so avoiding problems with categories that have similar characteristics. A second application, to genre ide...

Abstract Confidence limits are common place in physics analysis. Great care must be taken in their calculation and use, especially in cases of limited statistics when often one-sided limits are quoted. In order to estimate the stability of the confidence levels to addition of more data and/or change of cuts, we argue that the variance of their sampling distributions be calculated in addition to...