19. A storage medium tangibly embodying a program of machine-readable instructions executable by a digital processing apparatus to perform a method of processing an inductive learning model for 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.
19. A storage medium tangibly embodying a program of machine-readable instructions executable by a digital processing apparatus to perform a method of processing an inductive learning model for 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. 20. The storage medium of claim 19 , said method further comprising: if a desired adequacy of said ensemble model has not yet been reached, successively using another of said subsets of data to further refine said ensemble model until a desired adequacy of said ensemble model has been achieved, as indicated by said objective indication of adequacy; terminating said generating said learning model if one of a user termination instruction is received or said objective indication of adequacy crosses a predefined threshold; applying said learning model to other data stored in a memory, as based upon a user input instruction; and providing an output result of said applying said learning model to said other data.