{"id":150989,"date":"1990-06-01T00:00:00","date_gmt":"1990-06-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/conceptual-set-covering-improving-fit-and-split-algorithms\/"},"modified":"2018-10-16T20:15:30","modified_gmt":"2018-10-17T03:15:30","slug":"conceptual-set-covering-improving-fit-and-split-algorithms","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/conceptual-set-covering-improving-fit-and-split-algorithms\/","title":{"rendered":"Conceptual Set Covering: Improving Fit-and-Split Algorithms"},"content":{"rendered":"<div class=\"asset-content\">\n<p>Many learning systems implicitly use the fit-and-split learning method to create a comprehensive hypothesis from a set of partial hypotheses. At the core of the fit-and-split method is the assignment of examples to partial hypotheses. To date, however, this core has been neglected. This paper provides the first definition and model of the fit-and-split assignment problem. Extant systems perform assignment nearly arbitrarily, implicitly using, for example, greedy set covering. This paper also presents Conceptual Set Covering (CSC), a new assignment algorithm. An extensive empirical evaluation over a wide range of learning problems suggests that CSC can improve any fit-and-split learning system.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Many learning systems implicitly use the fit-and-split learning method to create a comprehensive hypothesis from a set of partial hypotheses. At the core of the fit-and-split method is the assignment of examples to partial hypotheses. To date, however, this core has been neglected. 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