Patent Document ID: 7904397
Application ID: 12690502

Base Claim:
1. A method of processing an inductive learning model for a database containing a dataset of examples, said method comprising: dividing said dataset of examples into a plurality of subsets of data; and generating, using a processor on a computer, a learning model using examples of a first subset of data of said plurality of subsets of data, wherein: said learning model being generated for said first subset comprises an initial stage of an evolving aggregate learning model (ensemble model) for an entirety of said dataset, said ensemble model thereby providing an evolving estimated learning model for the entirety of said dataset if all said subsets were to be processed, and said generating said learning model using data from a subset includes calculating a value for at least one parameter that provides an objective indication of an adequacy of a current stage of said ensemble model.

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Claim 5:
5. The method of claim 1 , wherein said at least one parameter comprises at least one of a current accuracy and an estimated final accuracy, said current accuracy indicating a calculated accuracy of said ensemble model at a current stage, said estimated final accuracy indicating a calculated estimated accuracy of said ensemble model if all of said subsets of data were to be processed.