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Abstract: The problem of how to learn from examples has been studied throughout the history of machine learning, and many successful learning algorithms have been developed. A problem that has received less attention is how to select which algorithm to use for a given learning task. The ability of a chosen algorithm to induce a good generalization depends on how appropriate the model class underlying the algorithm is for the given task. We define an algorithm's model class to be the representation... (Update)
Context of citations to this paper: More
...classifiers. Work by Utgoff and Brodley provide paradigm examples of recursive partitioning combination algorithms. Utgoff, 1989; Brodley, 1992] Utgoff s perceptron tree algorithm combined a univariate decision tree with linear threshold units. The algorithm first...
...for the instances that fall to that node. Work by Utgoff and Brodley provide paradigm examples of this approach [ Utgoff, 1989; Brodley, 1992 ] The important advantage of this approach is that a classifier can be selected whose bias is appropriate for a region of the instance...
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BibTeX entry: (Update)
Brodley, C.E. 1992. Dynamic Automatic Model Selection. Technical Report 92-30, Dept. of Computer Science, University of Massachusetts, Amherst, MA. http://citeseer.ist.psu.edu/brodley92dynamic.html More
@techreport{ brodley92dynamic,
author = "C. E. Brodley",
title = "Dynamic Automatic Model Selection",
number = "UM-CS-1992-030",
month = ",",
year = "1992",
url = "citeseer.ist.psu.edu/brodley92dynamic.html" }
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