Training neural networks with heterogeneous data.
Data pruning and ordered training are two methods and the results of a small theory that attempts to formalize neural network training with heterogeneous data. Data pruning is a simple process that attempts to remove noisy data. Ordered training is a more complex 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. Both methods derive from a set of premises that form the 'axiomatic' basis of our theory. 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 is presented in this paper along with a rough estimate of their effect on the overall multi-learner system. The handwriting data and the chosen language are Italian.
Drakopoulos
John A
JA
Tablet PC Handwriting Recognition Group, Microsoft Corporation, One Microsoft Way, Redmond, WA 98052-6399, USA. johndra@microsoft.com
Abdulkader
Ahmad
A
D000465:Algorithms; D001185:Artificial Intelligence; D002965:Classification; D003627:Data Interpretation, Statistical; D005716:Game Theory; D015233:Models, Statistical; D016571:Neural Networks, Computer