Classification model training using diverse training source and inference engine using same

Abstract

A method for generating a classification model using a training data set. An iterative procedure for training an ANN model, in which an iteration includes selecting a small sample of training data from a source of training data, training the model using the sample, using the model in inference mode over a larger sample of the training data, and reviewing the results of the inferencing. The results can be evaluated to determine whether the model is satisfactory, and if it does not meet specified criteria, then cycles of sampling, training, inferencing and reviewing results (STIR cycles) are repeated in an iterative process until the criteria are met. A classification engine trained as described herein is provided.