Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data. A simulation from public publication records: the people listed were not invited and did not agree to review.
? Tutorial for this page

Bernard W. Silverman

dblp:05/115 · DBLP profile ↗
← Back
5ranked-venue papers
2as first author
0since 2021 · last 1991
0000-0002-4059-2376ORCID · corroborated

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

Theory of computation · 3 · 1 first-authorArtificial intelligence and machine learning · 1 · 1 first-authorDatabases, data management, data science and information retrieval · 1Applied, interdisciplinary, general and emerging computing · 1

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 graphics and multimedia
1 paper
Image and video processing · 100%
Theoretical computer science
1 paper
Information theory · 100%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Image and video processing
edge detection
0.011990
The Specification of Edge Penalties for Regular and Irregular Pixel Images · IEEE Trans. Pattern Anal. Mach. Intell. 1990
Image and video processing
image segmentation
0.011990
The Specification of Edge Penalties for Regular and Irregular Pixel Images · IEEE Trans. Pattern Anal. Mach. Intell. 1990
Information theory
statistical inference
0.011980
Some asymptotic properties of the probabilistic teacher (Corresp.) · IEEE Trans. Inf. Theory 1980
Information theory › estimation theory
sequential estimation
0.011980
Some asymptotic properties of the probabilistic teacher (Corresp.) · IEEE Trans. Inf. Theory 1980

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

geometric analysis · 0.0statistical efficiency · 0.0posterior probability classification · 0.0consistency analysis · 0.0
YearPublicationVenuePosition
1991 Discretization effects in statistical inverse problems
Iain M. Johnstone, Bernard W. Silverman
J. Complex.2
1990 The Specification of Edge Penalties for Regular and Irregular Pixel Images
abstract
It is shown how some geometrical insights can be used to provide penalties for the various edge configurations in a way that is roughly independent of the pixel discretization. The penalties are consistent over pixels of different sizes, shapes, and orientations, event if these occur in the same pattern. The cases of square, rectangular, hexagonal, and irregular pixels are considered. In an experiment, the penalties perform substantially better than those previously proposed.>
Bernard W. Silverman, Christopher Jennison, Julian Stander, Timothy C. Brown
IEEE Trans. Pattern Anal. Mach. Intell.1
1980 Efficient Implementation of the Ada Overloading Rules
Peter J. L. Wallis, Bernard W. Silverman
Inf. Process. Lett.2
1980 Some asymptotic properties of the probabilistic teacher (Corresp.)
abstract
The problem of sequentially determining a mixture proportion is discussed, and the "probabilistic teacher" method, where successive observations are randomly classified according to current posterior probabilities, is investigated. The method is shown to be consistent and expressions for its statistical efficiency are obtained.
Bernard W. Silverman
IEEE Trans. Inf. Theory1
1979 Constructing the Convex Hull of a Set of Points in the Plane
abstract
A highly efficient algorithm for computing the vertices of the convex hull of a set of points in the plane is described and compared with existing methods. Empirical timings of a FORTRAN implementation of the algorithm show it to be faster than previous methods for most configurations of points. Careful consideration is given throughout to ensure that degenerate configurations, containing collinear or near-collinear points, are handled correctly.
P. J. Green, Bernard W. Silverman
Comput. J.2