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Igor Braga

dblp:137/4943 · DBLP profile ↗
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4ranked-venue papers
3as first author
0since 2021 · last 2020
—ORCID · unresolved

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

Artificial intelligence and machine learning · 2 · 2 first-authorDatabases, data management, data science and information retrieval · 1Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 first-authorApplied, interdisciplinary, general and emerging computing · 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.

Databases, data mining, and information retrieval
1 paper
Data mining · 100%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Data mining › statistical analysis › statistical estimation
density ratio estimation
0.212015
Stochastic Density Ratio Estimation and Its Application to Feature Selection · IJCAI 2015
Data mining › dimensionality reduction
feature selection
0.212015
Stochastic Density Ratio Estimation and Its Application to Feature Selection · IJCAI 2015

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

feature selection · 0.2density ratio estimation · 0.2
YearPublicationVenuePosition
2020 Does the Lean Inception Methodology Contribute to the Software Project Initiation Phase?
Igor Braga, Marcelo Nogueira, Nuno Santos 0002, Ricardo J. Machado 0001
ICCSA (6)1
2015 Stochastic Density Ratio Estimation and Its Application to Feature Selection
Igor Braga
IJCAI1
2015 Improving the kernel regularized least squares method for small-sample regression
Igor Braga, Maria Carolina Monard
Neurocomputing1
2014 A Constructive Setting for the Problem of Density Ratio Estimation
abstract
We introduce a constructive setting for the problem of density ratio estimation through the solution of a multidimensional integral equation. In this equation, not only its right hand side is approximately known, but also the integral operator is approximately defined. We show that this ill-posed problem has a rigorous solution and obtain the solution in a closed form. The key element of this solution is the novel V-matrix, which captures the geometry of the observed samples. We compare our method with previously proposed ones, using both synthetic and real data. Our experimental results demonstrate the good potential of the new approach.
Vladimir Vapnik, Igor Braga, Rauf Izmailov
SDM2