Journal article
Validation of machine learning techniques: decision trees and finite training set
Journal of Electronic Imaging, Vol.7(1), pp.94-103
1998
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.
Details
- Title
- Validation of machine learning techniques: decision trees and finite training set
- Authors/Creators
- C.P. Lam (Author/Creator) - Murdoch UniversityG.A.W. West (Author/Creator)T.M. Caelli (Author/Creator) - Curtin University
- Publication Details
- Journal of Electronic Imaging, Vol.7(1), pp.94-103
- Publisher
- SPIE
- Identifiers
- 991005540894907891
- Copyright
- © 1998 SPIE and IS&T.
- Murdoch Affiliation
- School of Engineering
- Language
- English
- Resource Type
- Journal article
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InCites Highlights
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- Collaboration types
- Domestic collaboration
- Citation topics
- 4 Electrical Engineering, Electronics & Computer Science
- 4.17 Computer Vision & Graphics
- 4.17.245 3D Geometry Processing
- Web Of Science research areas
- Engineering, Electrical & Electronic
- Imaging Science & Photographic Technology
- Optics
- ESI research areas
- Engineering