The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Cet article propose un algorithme semi-supervisé hors ligne efficace sur le plan informatique, qui produit une prédiction plus précise que l'algorithme de propagation d'étiquettes, couramment utilisé dans l'apprentissage semi-supervisé (SSL) basé sur des graphes en ligne. La méthode que nous proposons est une méthode hors ligne destinée à assister les algorithmes SSL basés sur des graphiques en ligne. L'efficacité de l'outil dans la création de nouveaux algorithmes d'apprentissage de ce type est démontrée par des expériences numériques.
Yuichiro WADA
Nagoya University
Siqiang SU
The Hong Kong Polytechnic University
Wataru KUMAGAI
RIKEN AIP
Takafumi KANAMORI
RIKEN AIP,Tokyo Institute of Technology
The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.
Copier
Yuichiro WADA, Siqiang SU, Wataru KUMAGAI, Takafumi KANAMORI, "Robust Label Prediction via Label Propagation and Geodesic k-Nearest Neighbor in Online Semi-Supervised Learning" in IEICE TRANSACTIONS on Information,
vol. E102-D, no. 8, pp. 1537-1545, August 2019, doi: 10.1587/transinf.2018EDP7424.
Abstract: This paper proposes a computationally efficient offline semi-supervised algorithm that yields a more accurate prediction than the label propagation algorithm, which is commonly used in online graph-based semi-supervised learning (SSL). Our proposed method is an offline method that is intended to assist online graph-based SSL algorithms. The efficacy of the tool in creating new learning algorithms of this type is demonstrated in numerical experiments.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2018EDP7424/_p
Copier
@ARTICLE{e102-d_8_1537,
author={Yuichiro WADA, Siqiang SU, Wataru KUMAGAI, Takafumi KANAMORI, },
journal={IEICE TRANSACTIONS on Information},
title={Robust Label Prediction via Label Propagation and Geodesic k-Nearest Neighbor in Online Semi-Supervised Learning},
year={2019},
volume={E102-D},
number={8},
pages={1537-1545},
abstract={This paper proposes a computationally efficient offline semi-supervised algorithm that yields a more accurate prediction than the label propagation algorithm, which is commonly used in online graph-based semi-supervised learning (SSL). Our proposed method is an offline method that is intended to assist online graph-based SSL algorithms. The efficacy of the tool in creating new learning algorithms of this type is demonstrated in numerical experiments.},
keywords={},
doi={10.1587/transinf.2018EDP7424},
ISSN={1745-1361},
month={August},}
Copier
TY - JOUR
TI - Robust Label Prediction via Label Propagation and Geodesic k-Nearest Neighbor in Online Semi-Supervised Learning
T2 - IEICE TRANSACTIONS on Information
SP - 1537
EP - 1545
AU - Yuichiro WADA
AU - Siqiang SU
AU - Wataru KUMAGAI
AU - Takafumi KANAMORI
PY - 2019
DO - 10.1587/transinf.2018EDP7424
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E102-D
IS - 8
JA - IEICE TRANSACTIONS on Information
Y1 - August 2019
AB - This paper proposes a computationally efficient offline semi-supervised algorithm that yields a more accurate prediction than the label propagation algorithm, which is commonly used in online graph-based semi-supervised learning (SSL). Our proposed method is an offline method that is intended to assist online graph-based SSL algorithms. The efficacy of the tool in creating new learning algorithms of this type is demonstrated in numerical experiments.
ER -