Patent ID: 7593908

Claim:
A computer implemented method of formalizing neural network training with heterogeneous data, the method comprising: employing at least one processor to execute computer executable instructions stored on at least one computer readable medium to perform the following acts: partitioning the heterogeneous data into a plurality of data groups, the heterogeneous data includes at least one of electronic ink or speech data that is employed to train a data recognition system; receiving an indication of relative importance of each data group and an order exponent of training for each group; creating a training data stream, wherein a distribution of data samples in the training data stream is a function of the distribution of assigned training iterations as specified by an ordered training model that is employed to transform the heterogeneous data to a computer recognizable character code, wherein the distribution of assigned training iterations is based in part on the order of training and the relative importance of each category; and generating a data file to train a data recognition module based in part on the training data stream.