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 8:
8. The method of claim 1 , further comprising progressively forming said ensemble model of said dataset, using said processor, by sequentially generating a learning model for each of a successive one of said plurality of subsets, until one of: a value calculated for said at least one parameter crosses a predefined threshold that indicates that an adequate estimated learning model for the entirety of the dataset has been achieved; said processing is terminated by a user input; and all subsets of said plurality of subsets have been processed in said inductive learning model.