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Oversearching and Layered Search in Empirical Learning (1995)  (Make Corrections)  (36 citations)
J. R. Quinlan, R. M. Cameron-Jones
IJCAI



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Abstract: When learning classifiers, more extensive search for rules is shown to lead to lower predictive accuracy on many of the real-world domains investigated. This counter-intuitive result is particularly relevant to recent systematic search methods that use risk-free pruning to achieve the same outcome as exhaustive search. We propose an iterated search method that commences with greedy search, extending its scope at each iteration until a stopping criterion is satisfied. This layered search is... (Update)

Context of citations to this paper:   More

.... methods attempt to establish a decision at a node by analyzing the classifiability of instances along each of the branches of a split [19] [21] Surprisingly, mixed results (ranging from look ahead makes no difference to look ahead produces larger trees [21] are reported...

...j that is identical to a state F i except for the order in which features appear, e.g. F j = x 3 x 5 x 1 . 2. A global pruning technique [27, 17], Line 5, Fig. 5. Define F best as the best explored monomial according to some impurity measure H (e.g. entropy) such that, for...

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Statistically Sound Exploratory Rule Discovery - Webb   (Correct)
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BibTeX entry:   (Update)

Quinlan, J. R. and Cameron-Jones, R. M. Oversearching and layered search in empirical learning. In Proceedings of the 14th International Joint Conference on Artificial Intelligence, pages 1019--1024, Montreal, Canada, August 1995. http://citeseer.ist.psu.edu/quinlan95oversearching.html   More

@inproceedings{ quinlan95oversearching,
    author = "J. Ross Quinlan and R. Mike Cameron-Jones",
    title = "Oversearching and Layered Search in Empirical Learning",
    booktitle = "{IJCAI}",
    pages = "1019-1024",
    year = "1995",
    url = "citeseer.ist.psu.edu/quinlan95oversearching.html" }
Citations (may not include all citations):
1262   Classification and Regression Trees (context) - Breiman, Friedman et al. - 1984
281   Programs for Machine Learning (context) - Quinlan - 1993
233   The cn2 induction algorithm - Clark, Niblett - 1989
149   Information Processing Letters (context) - Blumer, Ehrenfeucht et al. - 1987
87   Overfitting avoidance as bias - Schaffer - 1993
55   Pattern recognition as rule-guided inductive inference (context) - Michalski - 1980
46   Learning logical definitions from relations (context) - Quinlan - 1990
42   Efficiently inducing determinations: a complete and systemat.. - Schlimmer - 1993
38   Exploring the decision forest: an empirical investigation of.. - Murphy, Pazzani - 1994
31   An SE-tree based characterization of the induction problem - Rymon - 1993
25   Generalization as search (context) - Mitchell - 1982
19   Inferring decision trees using the Minimum Description Lengt.. (context) - Quinlan, Rivest - 1989
15   Systematic search for categorical attribute-value data-drive.. - Webb - 1993
6   Minimum description length instance-based learning (context) - Cameron-Jones - 1992
1   and Philip Stone (context) - Hunt, Marin - 1966



The graph only includes citing articles where the year of publication is known.


Documents on the same site (http://www.cs.jhu.edu/~salzberg/cs661.html):   More
Towards a Better Understanding of Memory-Based Reasoning Systems - Rachlin (1994)   (Correct)
A System for Induction of Oblique Decision Trees - Murthy, Kasif, Salzberg (1994)   (Correct)
MDL and Categorical Theories (Continued) - Quinlan (1995)   (Correct)

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