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)
This paper presents two new functional decomposition partitioning algorithms that use multivalued decision diagrams (MDDs). MDDs are an exceptionally good representation for generalized decomposition because they are canonical and they can represent very large functions. Algorithms developed in this paper are for Boolean/multivalued input and output,(More)
We present an implicit approach to solve problems arising in decomposition of incompletely specified multi-valued functions and relations. We introduce a new representation based on binary-encoded multi-valued decision diagrams (BEMDDs). This representation shares desirable properties of MDDs, in particular, compactness, and is applicable to(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 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)
This paper gives an overview of methods proposed for implementing multi-valued logic in CMOS and then describes Intrinsity’s patented Fast14 R © Technology as a mature methodology for silicon implementation of multi-valued logic. To the authors’ knowledge, no previous method of implementing multi-valued logic has been demonstrated with a design of the(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)