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Frank J. Iannarilli Jr.

dblp:13/1917 · DBLP profile ↗
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1ranked-venue papers
1as first author
0since 2021 · last 2003
—ORCID · none

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

Artificial intelligence and machine learning · 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.

Artificial intelligence
1 paper
Representation and self-supervised learning · 100%
Theoretical computer science
1 paper
Mathematical optimization · 100%

Topics — the 3 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Machine learning › Representation and self-supervised learning › representation learning › dimensionality reduction
feature selection		 0.012003
Feature Selection for Multiclass Discrimination via Mixed-Integer Linear Programming · IEEE Trans. Pattern Anal. Mach. Intell. 2003
Machine learning › Representation and self-supervised learning › representation learning › dimensionality reduction › feature selection
multi-class feature selection		 0.012003
Feature Selection for Multiclass Discrimination via Mixed-Integer Linear Programming · IEEE Trans. Pattern Anal. Mach. Intell. 2003
Mathematical optimization › discrete optimization
mixed integer linear programming		 0.012003
Feature Selection for Multiclass Discrimination via Mixed-Integer Linear Programming · IEEE Trans. Pattern Anal. Mach. Intell. 2003

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

branch-and-bound · 0.1mixed-integer linear programming · 0.0mixed integer linear programming · 0.0
YearPublicationVenuePosition
2003 Feature Selection for Multiclass Discrimination via Mixed-Integer Linear Programming
abstract
We reformulate branch-and-bound feature selection employing L/sub /spl infin// or particular L/sub p/ metrics, as mixed-integer linear programming (MILP) problems, affording convenience of widely available MILP solvers. These formulations offer direct influence over individual pairwise interclass margins, which is useful for feature selection in multiclass settings.
Frank J. Iannarilli Jr., Paul A. Rubin
 IEEE Trans. Pattern Anal. Mach. Intell.1