Patent attributes
Performance of the machine learning technique is assessed using Bayesian analysis where previously grouped documents belonging to a machine-assigned class or cluster are presented to a human rater and the rater's assessment is fed to the Bayesian analysis processor that updates a Beta bionomial model with each document. The model represents the precision probability associated with the class or cluster under test. Monitoring the precision probability, the technique enforces a set of stopping rules corresponding to an acceptance/rejection assessment of the machine learning apparatus. A Markov Chain Monte Carlo process operates on the model to infuse the processing of each subsequent class or cluster with knowledge from those previously processed.