1 January 1998 Validation of machine learning techniques: decision trees and finite training set
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J. of Electronic Imaging, 7(1), (1998). doi:10.1117/1.482630
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.
C. P. Lam, Geoffrey A. W. West, Terry M. Caelli, "Validation of machine learning techniques: decision trees and finite training set," Journal of Electronic Imaging 7(1), (1 January 1998). http://dx.doi.org/10.1117/1.482630
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KEYWORDS
Binary data

Legal

Machine learning

Data modeling

Feature extraction

Image processing

Image segmentation

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