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Robert L. Burr

dblp:73/5789 · DBLP profile ↗
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3ranked-venue papers
3as first author
0since 2021 · last 1989
0000-0001-7869-8857ORCID · corroborated

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

Human-computer interaction and ubiquitous computing · 2 · 2 first-authorTheory of computation · 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.

Theoretical computer science
1 paper
Information theory · 100%

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

TopicWeightPapersLastEvidence papers
Information theory › information measures › entropy
maximum entropy		 0.011989
Iterative convex I-projection algorithms for maximum entropy and minimum cross-entropy computations · IEEE Trans. Inf. Theory 1989
Information theory
minimum cross-entropy		 0.011989
Iterative convex I-projection algorithms for maximum entropy and minimum cross-entropy computations · IEEE Trans. Inf. Theory 1989
Information theory › signal processing
spectral estimation		 0.011989
Iterative convex I-projection algorithms for maximum entropy and minimum cross-entropy computations · IEEE Trans. Inf. Theory 1989

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

iterative projection · 0.0convex optimization · 0.0
YearPublicationVenuePosition
1989 Iterative convex I-projection algorithms for maximum entropy and minimum cross-entropy computations
abstract
General multiple-constraint minimum-cross-entropy and maximum-entropy problems can be solved by the iterative solution of single constraint subproblems. Existing theoretical results justify iterative I-projection algorithms that may be computationally expedient in some cases. An iterative algorithm for J.E. Shore's (ibid., vol.IT-28, no.6, p.956-61, Nov. 1982) minimum cross-entropy spectral analysis is developed as an example.>
Robert L. Burr
 IEEE Trans. Inf. Theory1
1988 Merging aggregate catch data with uncertain prior knowledge to approximate age and size distributions and selectivity functions
abstract
The problem of determining fish age and size distributions and gear selectivity from aggregate data is approached as an ill-posed inverse problem. Minimum cross-entropy inversion techniques allow the selection of a reasonable unique solution, directly incorporating background information of uncertain quality.>
Robert L. Burr
 IEEE Trans. Syst. Man Cybern.1
1988 Minimum cross-entropy spectral analysis of time-varying biological signals
abstract
The application of minimum cross-entropy (MCE) spectral analysis to successive blocks of biological time series is considered. Examples demonstrate the effectiveness of the MCE procedure for the analysis of cardiovascular ultrasound return and low-frequency electroencephalograph (EEG) signals.>
Robert L. Burr, Dean W. Lytle
 IEEE Trans. Syst. Man Cybern.1