Techniques for evaluation and/or retraining of a classification model built using labeled training data. In some aspects, a classification model having a first set of weights is retrained by using unlabeled input to reweight the labeled training data to have a second set of weights, and by retraining the classification model using the labeled training data weighted according to the second set of weights. In some aspects, a classification model is evaluated by building a similarity model that represents similarities between unlabeled input and the labeled training data and using the similarity model to evaluate the labeled training data to identify a subset of the plurality of items of labeled training data that is more similar to the unlabeled input than a remainder of the labeled training data.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. 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; 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. 2. The method of claim 1, wherein the act (B) comprises using the unlabeled input to reweight the labeled training data by changing weight values for at least a subset of the labeled training data. 3. 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; 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. 4. The tangible computer-recordable medium of claim 3, wherein the act (B) comprises using the unlabeled input to reweight the labeled training data by changing weight values for at least a subset of the labeled training data.