Evaluation of signature extension algorithms

Abstract

The author has identified the following significant results. One of the major findings was that nearly all of the bias in the proportion estimates of the multisegment training and classification procedure resulted from the particular configuration of the signature set used for classification, rather than from peculiarities of the recognition sample segments. This meant that the proportion estimation bias could be accurately corrected simply by estimating the bias on the original six training segments. The bias corrected proportion estimates of the multisegment training and classification procedure were extremely accurate and had a low variance when compared to local training and classification. This finding may have important ramifications for reducing the cost and increasing the accuracy of bias correction procedures.