Bikas Kumar Sinha. Sequential methods for finite populations. In B. K. Ghosh and P. K. Sen, editors, Handbook of Sequential Analysis, chapter 1, pages 1--19. Marcel Dekker, New York, 1991.  Home/Search   Document Not in Database   Summary   Related Articles

....finite training set using a single classifier. mating how likely a classifier consistent with the previously labeled data would be to produce the correct class label for a given unlabeled instance. These approaches can be viewed as a combination of stratified and sequential approaches to sampling [5, 32], so we refer to them as uncertainty sampling. A simple form of uncertainty sampling is possible for classifiers that operate by testing a numeric score against a threshold. A single classifier is trained, and those instances whose scores are closest to that classifier s threshold are good ....

....new algorithms on previously generated uncertainty samples. A better understanding of how to minimize the problems caused by a heterogeneous approach would be desirable. Note that we treated our large but finite set of instances as if it were infinite. By adapting results from sequential sampling [32] it may be possible both to improve uncertainty sampling and to tell when additional iterations are no longer providing any benefit when all the juice has been squeezed out of a data set. Finally, in contrast to the assumptions made in most theoretical work on querying, our categories are ....

Bikas Kumar Sinha. Sequential methods for finite populations. In B. K. Ghosh and P. K. Sen, editors, Handbook of Sequential Analysis, chapter 1, pages 1--19. Marcel Dekker, New York, 1991.

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