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More Efficient Windowing (1997)  (Make Corrections)  
Johannes Fürnkranz



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Abstract: Windowing has been proposed as a procedure for efficient memory use in the ID3 decision tree learning algorithm. However, previous work has shown that windowing may often lead to a decrease in performance. In this work, we try to argue that separate-and-conquer rule learning algorithms are more appropriate for windowing than divide-and-conquer algorithms, because they learn rules independently and are less susceptible to changes in class distributions. In particular, we will present a new... (Update)

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BibTeX entry:   (Update)

Johannes Furnkranz. More efficient windowing. In Proceedings of the 14th National Conference on Artificial Intelligence (AAAI-97), Providence, RI, 1997. AAAI Press. In press. http://citeseer.ist.psu.edu/60863.html   More

@misc{ furnkranz97more,
  author = "J. Furnkranz",
  title = "More efficient windowing",
  text = "Johannes Furnkranz. More efficient windowing. In Proceedings of the 14th
    National Conference on Artificial Intelligence (AAAI-97), Providence, RI,
    1997. AAAI Press. In press.",
  year = "1997",
  url = "citeseer.ist.psu.edu/60863.html" }
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492   Learning logical definitions from relations (context) - Quinlan - 1990  ACM   DBLP
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44   An experimental comparison of human and machine learning for.. - Muggleton, Bain et al. - 1989  DBLP
33   Incremental Reduced Error Pruning (context) - Furnkranz, Widmer - 1994  DBLP
13   Separate-and-conquer rule learning - Furnkranz - 1996  ACM   DBLP
10   Sampling strategies and learning efficiency in text categori.. - Yang - 1996
9   Experiments on the costs and benefits of windowing in ID3 (context) - Wirth, Catlett - 1988  DBLP

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