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An Incremental Method for Finding Multivariate Splits for Decision Trees (1990)  (Make Corrections)  (19 citations)
Paul E. Utgoff, Carla E. Brodley
Machine Learning



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Abstract: Decision trees that are limited to testing a single variable at a node are potentially much larger than trees that allow testing multiple variables at a node. This limitation reduces the ability to express concepts succinctly, which renders many classes of concepts difficult or impossible to express. This paper presents the PT2 algorithm, which searches for a multivariate split at each node. Because a univariate test is a special case of a multivariate test, the expressive power of such... (Update)

Context of citations to this paper:   More

.... just above the leaf nodes, were discussed in [480] Decision trees with perceptrons at all internal nodes were described in [482, 438]. Mathematical Programming: Linear programming has been used for building adaptive classifiers since late 1960s [216] Given two...

...a fast method is desired. Ideally, an iterative method is also desirable in case new points are added and the tree needs to be adjusted [21]. Previous iterative approaches based on extensions to the perceptron algorithm [19, 6, 7] do not have stable performance for the...

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11:   Pattern classification and scene analysis (context) - Duda, Hart - 1973
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BibTeX entry:   (Update)

Utgoff, P. E., & Brodley, C. E. (1990). An incremental method for finding multivariate splits for decision trees. In Proceedings of the Seventh International Conference on Machine Learning (pp. 58--65). http://citeseer.ist.psu.edu/utgoff90incremental.html   More

@inproceedings{ utgoff90incremental,
    author = "Paul E. Utgoff and Carla E. Brodley",
    title = "An Incremental Method for Finding Multivariate Splits for Decision Trees",
    booktitle = "Machine Learning",
    pages = "58-65",
    year = "1990",
    url = "citeseer.ist.psu.edu/utgoff90incremental.html" }
Citations (may not include all citations):
2133   Pattern classification and scene analysis (context) - Duda, Hart - 1973
1359   Induction of decision trees (context) - Quinlan - 1986
1262   Classification and regression trees (context) - Breiman, Friedman et al. - 1984
180   The CN2 induction algorithm (context) - Clark, Niblett - 1989
102   An empirical comparison of pruning methods for decision tree.. (context) - Mingers - 1989
88   Learning machines (context) - Nilsson - 1965
59   Unknown attribute values in induction - Quinlan - 1989
51   Decision trees and diagrams (context) - Moret - 1982
50   An experimental comparison of symbolic and connectionist lea.. (context) - Mooney, Shavlik et al. - 1989
48   Classifier systems and the animat problem (context) - Wilson - 1987
43   Perceptron trees: A case study in hybrid concept representat.. (context) - Utgoff - 1988
43   Perceptron trees: A case study in hybrid concept representat.. (context) - Utgoff - 1989
30   Learning DNF by decision trees (context) - Pagallo - 1983
28   Learning by statistical cooperation of self-interested neuro.. (context) - Barto - 1985
26   Decision trees as probabilistic classifiers (context) - Quinlan - 1987
22   Optimal linear discriminants (context) - Gallant - 1986
21   Improved decision trees: A generalized version of ID3 (context) - Cheng, Fayyad et al. - 1988
21   An empirical comparison of genetic and decision-tree classif.. (context) - Quinlan - 1988
20   Linear function neurons: Structure and training (context) - Hampson, Volper - 1986
3   Feature discovery in empirical learning (context) - Pagallo, Haussler - 1988
2   Inductive learning with BCT - Chan - 1989



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