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Journal of Electronic Imaging

Validation of machine learning techniques: decision trees and finite training set
Author(s): C. P. Lam; Geoffrey A. W. West; Terry M. Caelli
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Paper Abstract

There has been some recent interest in using machine learning techniques as part of pattern recognition systems. However, little attention is typically given to the validity of the features and types of rules generated by these systems and how well they perform across a variety of features and patterns. We focus on such issues of validity and comparative performance using two different types of decision tree techniques. In addition, we introduce the notion of including legal perturbations of objects in the training set and show that the performance of the resulting classifiers was better than that those trained without such "legal" constructs in the data selection.

Paper Details

Date Published: 1 January 1998
PDF: 10 pages
J. Electron. Imaging. 7(1) doi: 10.1117/1.482630
Published in: Journal of Electronic Imaging Volume 7, Issue 1
Show Author Affiliations
C. P. Lam, Curtin Univ. of Technology (Australia)
Geoffrey A. W. West, Curtin Univ. of Technology (Australia)
Terry M. Caelli, Curtin Univ. of Technology (Australia)

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