Training with heterogeneous data

Data pruning and ordered training are two methods used to train a learner with heterogeneous data. The former is a typical procedure that attempts to factor out noise from the data; the latter is a novel method that partitions the data into a number of categories and assigns training times to those assuming that data size and training time have a polynomial relation. In its current form, ordered training is an approximate and a priori data-emphasizing method. Both methods have been applied to a time-delay neural network - which is one of the main learners in Microsoft´s Tablet PC handwriting recognition system. Their effect on the learner is presented in this paper. The handwriting data and the chosen language are Italian.