(Enter summary)
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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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" }
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