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Craig M. Files

dblp:66/1495 · DBLP profile ↗
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3ranked-venue papers
1as first author
0since 2021 · last 2004
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 2Systems, architecture and hardware · 1 · 1 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer architecture, parallel and distributed computing, and storage systems
1 paper
Electronic design automation · 67% Performance modeling and evaluation · 33%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Electronic design automation › logic synthesis
boolean function decomposition		 0.012000
New multivalued functional decomposition algorithms based on MDDs · IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 2000
Electronic design automation
logic synthesis		 0.012000
New multivalued functional decomposition algorithms based on MDDs · IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 2000
Performance modeling and evaluation
multi-valued decision diagram		 0.012000
New multivalued functional decomposition algorithms based on MDDs · IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 2000

Methods — techniques the papers use, named apart from their topics

decision diagram partitioning · 0.0
YearPublicationVenuePosition
2004 Decision Diagrams in Machine Learning: An Empirical Study on Real-Life Credit-Risk Data
Christophe Mues, Bart Baesens, Craig M. Files, Jan Vanthienen
 Diagrams3
2004 Decision diagrams in machine learning: an empirical study on real-life credit-risk data
Christophe Mues, Bart Baesens, Craig M. Files, Jan Vanthienen
 Expert Syst. Appl.3
2000 New multivalued functional decomposition algorithms based on MDDs
abstract
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, completely/incompletely specified functions with application to logic synthesis, machine learning, data mining and knowledge discovery in databases. We compare the run-times and decision diagram sizes of our algorithms to existing decomposition partitioning algorithms based on decision diagrams. The comparisons show that our algorithms are faster and do not result in exponential diagram sizes when decomposing functions with small bound sets.
Craig M. Files, Marek A. Perkowski
 IEEE Trans. Comput. Aided Des. Integr. Circuits Syst.1