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Exploiting Context When Learning to Classify (1993)  (Make Corrections)  (14 citations)
Peter Turney
Machine Learning: ECML-93, European Conference on Machine Learning, Proceedings



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Abstract: . This paper addresses the problem of classifying observations when features are context-sensitive, specifically when the testing set involves a context that is different from the training set. The paper begins with a precise definition of the problem, then general strategies are presented for enhancing the performance of classification algorithms on this type of problem. These strategies are tested on two domains. The first domain is the diagnosis of gas turbine engines. The problem is to... (Update)

Context of citations to this paper:   More

.... batches in the training set as coming from a different context and to use a context sensitive learning algorithm as suggested by Turney (1993), Widmer and Kubat (1996) and Kubat and Widmer (1996) However, our initial experiments with simple contextual normalization...

...this set is explored by the wrapper at some point during the search. 4 A longer discussion of contextual features may be found in Turney (1993), although the definitions originally given were found to be flawed as mentioned in Turney (1996) 5 The results in both papers by Aha...

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7:   Instance-Based Learning Algorithms (context) - Aha, Kibler et al. - 1991
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BibTeX entry:   (Update)

Turney, P. D. (1993). Exploiting context when learning to classify. In Proceedings of the European Conference on Machine Learning, pages 402--407. ftp://ai.iit.nrc.ca/pub/ksl-papers/NRC35058. http://citeseer.ist.psu.edu/turney93exploiting.html   More

@inproceedings{ turney93exploiting,
    author = "Peter D. Turney",
    title = "Exploiting Context When Learning to Classify",
    booktitle = "Machine Learning: {ECML}-93, European Conference on Machine Learning, Proceedings",
    volume = "667",
    publisher = "Springer-Verlag",
    pages = "402--407",
    year = "1993",
    url = "citeseer.ist.psu.edu/turney93exploiting.html" }
Citations (may not include all citations):
6   Lebiere: The Cascade-Correlation Learning Architecture (context) - Fahlman - 1991
4   Albert: Instance-based learning algorithms (context) - Aha, Kibler - 1991
2   Aha: UCI Repository of Machine Learning Databases (context) - Murphy - 1991
1   ication Techniques, (edited collection), Los Alamitos, CA: I.. (context) - Dasarathy, Pattern - 1991
1   Smith: Applied Regression Analysis (context) - Draper - 1981
1   Collins: Robust classifiers without robust features (context) - Katz, Gately - 1990
1   Halasz: Contextual normalization applied to aircraft gas tur.. (context) - Turney - 1993
1   UK: University of Cambridge (context) - Deterding, for et al. - 1989
1   UK: University of Cambridge (context) - Robinson, Propagation et al. - 1989
1   Albert: Instance-based prediction of real-valued attributes (context) - Kibler, Aha - 1989



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


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How to Shift Bias: Lessons from the Baldwin Effect - Peter Turney (1996)   (Correct)
Contextual Normalization Applied to Aircraft Gas Turbine Engine.. - Turney (1993)   (Correct)
Preprocessing by a cost-sensitive literal reduction.. - Lavrac, Gamberger.. (1996)   (Correct)

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