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Gustavo Belforte

dblp:96/5441 · DBLP profile ↗
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2ranked-venue papers
1as first author
0since 2021 · last 2007
—ORCID · none

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

Artificial intelligence and machine learning · 1 · 1 first-authorGraphics, computer vision, multimedia, augmented reality and games · 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
Computational fabrication · 100%
Artificial intelligence
1 paper
Probabilistic and Bayesian machine learning · 70% Motion planning and robot control · 30%

Topics — the 4 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Computational fabrication
computer-aided manufacturing		 0.112007
Systematic geometric rigid body error identification of 5-axis milling machines · Comput. Aided Des. 2007
Machine learning › Probabilistic and Bayesian machine learning › statistical inference › bayesian inference
bayesian classification		 0.011986
Conditional Allocation and Stopping Rules in Bayesian Pattern Recognition · IEEE Trans. Pattern Anal. Mach. Intell. 1986
Machine learning › Probabilistic and Bayesian machine learning › structured prediction
sequential classification		 0.011986
Conditional Allocation and Stopping Rules in Bayesian Pattern Recognition · IEEE Trans. Pattern Anal. Mach. Intell. 1986
Robotics › Motion planning and robot control
stopping criteria		 0.011986
Conditional Allocation and Stopping Rules in Bayesian Pattern Recognition · IEEE Trans. Pattern Anal. Mach. Intell. 1986

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

monte carlo simulation · 0.0bayesian inference · 0.0
YearPublicationVenuePosition
2007 Systematic geometric rigid body error identification of 5-axis milling machines
Erik L. J. Bohez, Bancha Ariyajunya, Chanin Sinlapeecheewa, Tin Maung Maung Shein, Do Tien Lap, Gustavo Belforte
 Comput. Aided Des.6
1986 Conditional Allocation and Stopping Rules in Bayesian Pattern Recognition
abstract
This paper considers the problem of stopping rules, in the context of sequential Bayesian classification. In particular a new criterion, based on the probability of reversal of the obtained classification, is introduced and compared to more commonly used strategies. The results show good behavior of the proposed technique, with both simulated and real data drawn from biomedical application. In fact it appears that this stopping rule reduces the misallocation error rate with the same mean number of used features, or conversely, with an equal level of misallocation error rate, it reduces the mean number of features necessary to attain it.
Gustavo Belforte, Basilio Bona, Roberto Tempo
 IEEE Trans. Pattern Anal. Mach. Intell.1