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Dynamic techniques for evaluating quality of clustering or classification system aimed to minimize the number of manual reviews based on Bayesian inference and Markov Chain Monte Carlo (MCMC) techniques
Dynamic techniques for evaluating quality of clustering or classification system aimed to minimize the number of manual reviews based on Bayesian inference and Markov Chain Monte Carlo (MCMC) techniques
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.
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