Craig M. Files

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In this paper, the minimization of incompletely specified multi-valued functions using functional decomposition is discussed. From the aspect of machine learning, learning samples can be implemented as minterms in multi-valued logic. The representation, can then be decomposed into smaller blocks, resulting in a reduced problem complexity. This gives induced(More)
Achieving complete delay fault testa-bility by extra inputs, " in Proc. A. Vardanian, " On completely robust path delay fault testable realization of logic functions, " in Proc. Synthesis of multi-level combinational circuits for complete robust path delay fault testability, " in Abstract—This paper presents two new functional decomposition partitioning(More)
This paper considers minimization of incompletely specified multi-valued functions using functional decomposition. While functional decomposition was originally created for the minimization of logic circuits, this paper uses the decomposition process for both machine learning and logic synthesis of multi-valued functions. As it turns out, the minimization(More)
Decision trees are a widely used knowledge representation in machine learning. However, one of their main drawbacks is the inherent replication of isomorphic subtrees, as a result of which the produced classifiers might become too large to be comprehensible by the human experts that have to validate them. Alternatively, decision diagrams, a generalization(More)
This paper presents a Multi-valued Decision Diagram (MDD) that has additional null-value and all-value edges. The MDD is based on a multi-valued algebra that augments multi-valued variables to allow a null output value. A null output value represents the lack of any valid value for a given input combination (akin to an output don't care, but its value(More)
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