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      •   Research Commons
      • University of Waikato Research
      • Computing and Mathematical Sciences
      • Computer Science Working Paper Series
      • 1996 Working Papers
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      •   Research Commons
      • University of Waikato Research
      • Computing and Mathematical Sciences
      • Computer Science Working Paper Series
      • 1996 Working Papers
      • View Item
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      Theory combination: an alternative to data combination

      Ting, Kai Ming; Low, Boon Toh
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      Ting, K. M. & Low, B. T. (1996). Theory combination: an alternative to data combination. (Working paper 96/19). Hamilton, New Zealand: University of Waikato, Department of Computer Science.
      Permanent Research Commons link: https://hdl.handle.net/10289/1172
      Abstract
      The approach of combining theories learned from multiple batches of data provide an alternative to the common practice of learning one theory from all the available data (i.e., the data combination approach). This paper empirically examines the base-line behaviour of the theory combination approach in classification tasks. We find that theory combination can lead to better performance even if the disjoint batches of data are drawn randomly from a larger sample, and relate the relative performance of the two approaches to the learning curve of the classifier used.
      Date
      1996-10
      Type
      Working Paper
      Series
      Computer Science Working Papers
      Report No.
      96/19
      Collections
      • 1996 Working Papers [32]
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