(Enter summary)
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)
Similar documents (at the sentence level):
57.3%: More Efficient Windowing - Fürnkranz (1997)
(Correct)
17.8%: Integrative Windowing - Fürnkranz (1998)
(Correct)
8.9%: Noise-Tolerant Windowing - Fürnkranz
(Correct)
Active bibliography (related documents): More All
0.2: Dimensionality Reduction in ILP: A Call To Arms - Fürnkranz
(Correct)
0.1: An Extensible Meta-Learning Approach for Scalable and Accurate.. - Chan (1996)
(Correct)
0.1: Toward Scalable and Parallel Inductive Learning: A Case Study in.. - Chan (1994)
(Correct)
Similar documents based on text: More All
0.6: Inductive Rule Learning for Data and Web Mining - Fürnkranz
(Correct)
0.3: Windowing versus Best Tiling for Wavelet Image Compression - Lee (1999)
(Correct)
0.3: A High Assurance Window System Prototype - Epstein, McHugh, Orman, Pascale, .. (1994)
(Correct)
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" }
Citations (may not include all citations):
2177
Programs for Machine Learning (context) - Quinlan - 1993
492
Learning logical definitions from relations (context) - Quinlan - 1990 ACM DBLP
248
Fast effective rule induction
- Cohen - 1995 DBLP
163
Learning efficient classification procedures and their appli.. (context) - Quinlan - 1983
75
Heterogeneous uncertainty sampling for supervised learning
- Lewis, Catlett - 1994
47
Megainduction: A test flight (context) - Catlett - 1991 DBLP
46
From data mining to knowledge discovery in databases
- Fayyad, Piatetsky-Shapiro et al. - 1996 DBLP
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
Documents on the same site (http://www.ai.univie.ac.at/~juffi/publications/): More
A Tight Integration of Pruning and Learning (Extended Abstract) - Fürnkranz
(Correct)
More Efficient Windowing - Fürnkranz (1997)
(Correct)
Fossil: A Robust Relational Learner - Fürnkranz
(Correct)
Online articles have much greater impact More about CiteSeer.IST Add search form to your site Submit documents Feedback
CiteSeer.IST - Copyright Penn State and NEC