Techniques for evaluation, building and/or retraining of a classification model

A tangible computer-recordable medium having a plurality of instructions embodied therein, wherein the plurality of instructions, when executed by a processor, cause a machine to perform a method of processing a first classification model that classifies an input into one of a plurality of classes, wherein the first classification model was built using labeled training data having a first set of weights, wherein each item of labeled training data is labeled with one of the plurality of classes, the method comprising acts of:

• (A) obtaining unlabeled input for the first classification model;
• (B) using the unlabeled input to reweight the labeled training data to have a second set of weights that is different from the first set of weights by:
• building a similarity model that represents similarities between the unlabeled input and the labeled training data and
• reweighting the labeled training data to have the second set of weights based on the similarities between the unlabeled input and the labeled training data indicated by the similarity model;
• and
• (C) using the labeled training data reweighted according to the second set of weights to:
• retrain the first classification model;
• and/or
• build a second classification model.