Training neural networks with heterogeneous data.

John A Drakopoulos; Ahmad Abdulkader

doi:10.1016/j.neunet.2005.06.011

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PubMed · 16095874

Training neural networks with heterogeneous data.

John A Drakopoulos·Ahmad Abdulkader

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Abstract

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.

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John A Drakopoulos, Ahmad Abdulkader. Training neural networks with heterogeneous data.. https://doi.org/10.1016/j.neunet.2005.06.011

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