%Aigaion2 BibTeX export from Knowledge Engineering Publications
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       author = {Weizs{\"{a}}cker, Lorenz and F{\"{u}}rnkranz, Johannes},
        title = {Margin Driven Separate and Conquer by Working Set Expansion},
       number = {TUD-KE-2009-06},
         year = {2009},
  institution = {TU Darmstadt, Knowledge Engineering Group},
          url = {http://www.ke.informatik.tu-darmstadt.de/publications/reports/tud-ke-2009-06.pdf},
     abstract = {Covering algorithms for binary classification build a list of one-sided partial models in a greedy manner. The original motivation therefor stems from the context of rule learning where the expressiveness of a single rule is too limited to serve as standalone model. If the model space is richer, the decomposition into subproblems is not strictly necessary but separately solved subproblems might still lead to better models specially when the subproblems are less  demanding in terms of the input model. We investigate in this direction with an AQR style covering algorithm that uses an SVM base learner for discovering the subproblems along with a corresponding output model. The experimental study covers different criteria for the selection of the subproblems and as well as several vector kernels of varying model capacity.}