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http://repositorio.unicamp.br/jspui/handle/REPOSIP/330115
Type: | Artigo |
Title: | Nearest Neighbors Distance Ratio Open-set Classifier Nearest neighbors distance ratio open-set classifier |
Author: | Mendes Junior, Pedro R. Souza, Roberto M. de Weneck, Rafael de O. Stein, Bernardo V. Pazinato, Daniel V. Almeida, Waldir R. de Penatti, Otávio A. B. Torres, Ricardo da S. Rocha, Anderson |
Abstract: | 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. 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 inher |
Subject: | Open-set Recognition Nearest Neighbor Classifier Open-set Nearest-neighbor Classifier Nearest Neighbors Distance Ratio Open-set Evaluation Measures Reconhecimento em cenário aberto Classificação multi-classe Reconhecimento de padrões Inteligência artificial |
Country: | Estados Unidos |
Editor: | Springer |
Citation: | Machine Learning. Springer, v. 106, p. 359 - 386, 2017. |
Rights: | fechado Aberto |
Identifier DOI: | 10.1007/s10994-016-5610-8 |
Address: | https://link.springer.com/article/10.1007/s10994-016-5610-8 |
Date Issue: | 2017 |
Appears in Collections: | IC - Artigos e Outros Documentos FEA - Artigos e Outros Documentos FEEC - Artigos e Outros Documentos |
Files in This Item:
File | Size | Format | |
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000394355700002.pdf | 4.85 MB | Adobe PDF | View/Open |
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