Communications of the ACM - Special issue on parallelism
Set operations on polyhedra using binary space partitioning trees
SIGGRAPH '87 Proceedings of the 14th annual conference on Computer graphics and interactive techniques
Perceptrons: expanded edition
Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Computers and Biomedical Research
A geometric framework for machine learning
A geometric framework for machine learning
Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
Machine Learning - Special issue on learning with probabilistic representations
System Test and Diagnosis
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Introduction to Algorithms
System Complexity and Integrated Diagnostics
IEEE Design & Test
Machine Learning
Improving the accuracy of diagnostics provided by fault dictionaries
VTS '96 Proceedings of the 14th IEEE VLSI Test Symposium
Constraint Processing
On growing better decision trees from data
On growing better decision trees from data
The representational power of discrete bayesian networks
The Journal of Machine Learning Research
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
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As new approaches and algorithms are developed for system diagnosis, it is important to reflect on existing approaches to determine their strengths and weaknesses. Of concern is identifying potential reasons for false pulls during maintenance. Within the aerospace community, one approach to system diagnosis--based on the D-matrix derived from test dependency modeling--is used widely, yet little has been done to perform any theoretical assessment of the merits of the approach. Past assessments have been limited, largely, to empirical analysis and case studies. In this paper, we provide a theoretical assessment of the representation power of the D-matrix and suggest algorithms and model types for which the D-matrix is appropriate. We also prove a surprising result relative to the difficulty of generating optimal diagnostic strategies from D-matrices. Finally, we relate the processing of the D-matrix with several diagnostic approaches and suggest how to extend the power of the D-matrix to take advantage of the power of those approaches.