Patent ID: 11893772
Assignee: AMAZON TECHNOLOGIES, INC.
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 5:
6. A method, comprising:
performing, at one or more computing devices:
executing a plurality of multi-phase learning iterations to train successive versions of a classification model on selected computing resources using a source data set of a plurality of records until a training completion criterion is met, wherein individual ones of the multi-phase learning iterations include at least (a) a bucket group selection phase and (b) a class boundary refinement phase, and the training completion criterion limits execution of training by a computing resource consumption limit or execution time limit,
wherein the bucket group selection phase comprises identifying one or more positive-match buckets from among unlabeled buckets in a plurality of buckets, wherein individual ones of the buckets comprise a respective subset of the source data set, wherein respective ones of the positive-match buckets include one or more records labeled as members of a target class of one or more target classes, wherein the bucket group selection phase eliminates one or more other buckets in the plurality of buckets for annotation to avoid wasting computing resources, wherein at least one bucket is selected as a candidate for annotation as a positive-match bucket based at least in part on output obtained from a first version of the classification model generated in the class boundary refinement phase of an earlier learning iteration; and
wherein the class boundary refinement phase comprises training a second version of the classification model using a training data set including at least a subset of records of the positive-match buckets identified in the bucket group selection phase, wherein at least one record of the subset is identified as a candidate for inclusion in the training data set based at least in part on a model enhancement potential criterion; and

stopping execution of the training when the training completion criterion is met and storing a trained version of the classification model produced by the training.