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Nearest neighbors distance ratio open-set classifier
by
Rocha, Anderson
, Penatti, Otávio A. B.
, Mendes Júnior, Pedro R.
, de Almeida, Waldir R.
, Stein, Bernardo V.
, Torres, Ricardo da S.
, de Souza, Roberto M.
, Pazinato, Daniel V.
, Werneck, Rafael de O.
in
Artificial Intelligence
/ Benchmarks
/ Classification
/ Classifiers
/ Computer Science
/ Control
/ Machine learning
/ Mechatronics
/ Natural Language Processing (NLP)
/ Parameters
/ Recognition
/ Resilience
/ Robotics
/ Simulation and Modeling
/ Test procedures
/ Training
2017
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Nearest neighbors distance ratio open-set classifier
by
Rocha, Anderson
, Penatti, Otávio A. B.
, Mendes Júnior, Pedro R.
, de Almeida, Waldir R.
, Stein, Bernardo V.
, Torres, Ricardo da S.
, de Souza, Roberto M.
, Pazinato, Daniel V.
, Werneck, Rafael de O.
in
Artificial Intelligence
/ Benchmarks
/ Classification
/ Classifiers
/ Computer Science
/ Control
/ Machine learning
/ Mechatronics
/ Natural Language Processing (NLP)
/ Parameters
/ Recognition
/ Resilience
/ Robotics
/ Simulation and Modeling
/ Test procedures
/ Training
2017
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Nearest neighbors distance ratio open-set classifier
by
Rocha, Anderson
, Penatti, Otávio A. B.
, Mendes Júnior, Pedro R.
, de Almeida, Waldir R.
, Stein, Bernardo V.
, Torres, Ricardo da S.
, de Souza, Roberto M.
, Pazinato, Daniel V.
, Werneck, Rafael de O.
in
Artificial Intelligence
/ Benchmarks
/ Classification
/ Classifiers
/ Computer Science
/ Control
/ Machine learning
/ Mechatronics
/ Natural Language Processing (NLP)
/ Parameters
/ Recognition
/ Resilience
/ Robotics
/ Simulation and Modeling
/ Test procedures
/ Training
2017
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Journal Article
Nearest neighbors distance ratio open-set classifier
2017
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Overview
In this paper, we propose a novel multiclass classifier for the open-set recognition scenario. This scenario is the one in which there are no a priori training samples for some classes that might appear during testing. Usually, many applications are inherently open set. Consequently, successful closed-set solutions in the literature are not always suitable for real-world recognition problems. The proposed open-set classifier extends upon the Nearest-Neighbor (NN) classifier. Nearest neighbors are simple, parameter independent, multiclass, and widely used for closed-set problems. The proposed Open-Set NN (OSNN) method incorporates the ability of recognizing samples belonging to classes that are unknown at training time, being suitable for open-set recognition. In addition, we explore evaluation measures for open-set problems, properly measuring the resilience of methods to unknown classes during testing. For validation, we consider large freely-available benchmarks with different open-set recognition regimes and demonstrate that the proposed OSNN significantly outperforms their counterparts in the literature.
Publisher
Springer US,Springer Nature B.V
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