Abstract:
The recently introduced transductive confidence machines (TCMs) framework allows
to extend classifiers such that their performance can be set by the user prior to classification. In this paper we apply the TCM framework, with a plugged in k-nearest
neighbor classifer, to the domain of (on-line) handwriting recognition. First, we modify
the original TCM algorithm to make it much more efficient. Then, we use this modified
algorithm to classify the NicIcon database of iconic gestures. Results show that the
modified TCM algorithm is a promising way to classify handwriting.