Patent ID: 7587374

Claim:
A method of training a mean-field Bayesian data reduction classifier implemented on a computer with available training data having target data points and non-target data points, said method comprising the steps of: selecting a best initial number of levels; training the mean-field Bayesian data reduction classifier once for each target data point at the best initial number of levels with the subject target data point labeled and all other points unlabeled; computing a cluster training error while training the mean-field Bayesian data reduction classifier for each target data point so that each target data point has an associated cluster training error; sorting target data points by associated cluster training error so that target data points having the same cluster training error are grouped together; selecting a cluster candidate as grouped target data points having a low cluster training error and a high number of target data points, the low cluster training error being identified as a first cluster training error; training the mean-field Bayesian data reduction classifier at the selected best initial number of levels with the cluster candidate points labeled and unlabeling remaining target data points; computing a second cluster training error while training the mean-field Bayesian data reduction classifier for the cluster candidate points; comparing the second cluster training error with the first cluster training error; confirming the cluster candidate if the first cluster training error is greater than the second cluster candidate training error; and producing a trained Bayesian Data Reduction classifier capable of identifying clusters in unlabeled data from said confirmed cluster candidate.