1. An automated method for classification of samples performed by a processing device, said method comprising: (a) providing to said processing device a trained statistical model based upon a set of initial samples; (b) said processing device receiving a first set of samples, wherein each of said first set of samples includes a label thereof provided by a user indicating characteristics of each of said first set of samples, and each of said labels thereof includes an appended respective confidence measure provided by said user to the label of each labeled said sample; (c) said processing device training a first statistical model based upon selective ones of said first set of samples having a respective said appended confidence measure above a threshold, said first statistical model being of the same class as said trained statistical model; (d) said processing device receiving a second set of samples that do not include a label thereof indicating characteristics of each of said second set of samples provided by said user and do not include for each of said labels an appended respective confidence measure provided by said user and updating said first statistical model based on said second set of samples; (e) said processing device training a second statistical model based upon said second set of samples, said second statistical model being of the same class as said trained statistical model; (f) wherein said trained statistical model, said first statistical model, and said second statistical model, are independent of each other and collectively used by said processing device to classify another sample.