Gotowe bibliografie tematyczne / Multi-labels

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  2. Rozprawy doktorskie
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Gotowa bibliografia na temat „Multi-labels”

Autor: Grafiati
Data publikacji: 24 sierpnia 2024
Data aktualizacji: 31 lipca 2025

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Artykuły w czasopismach na temat "Multi-labels"

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Lee, Seongmin, Hyunsik Jeon, and U. Kang. "Multi-EPL: Accurate multi-source domain adaptation." PLOS ONE 16, no. 8 (2021): e0255754. http://dx.doi.org/10.1371/journal.pone.0255754.

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Given multiple source datasets with labels, how can we train a target model with no labeled data? Multi-source domain adaptation (MSDA) aims to train a model using multiple source datasets different from a target dataset in the absence of target data labels. MSDA is a crucial problem applicable to many practical cases where labels for the target data are unavailable due to privacy issues. Existing MSDA frameworks are limited since they align data without considering labels of the features of each domain. They also do not fully utilize the target data without labels and rely on limited feature
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Hao, Pingting, Kunpeng Liu, and Wanfu Gao. "Double-Layer Hybrid-Label Identification Feature Selection for Multi-View Multi-Label Learning." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 11 (2024): 12295–303. http://dx.doi.org/10.1609/aaai.v38i11.29120.

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Multi-view multi-label feature selection aims to select informative features where the data are collected from multiple sources with multiple interdependent class labels. For fully exploiting multi-view information, most prior works mainly focus on the common part in the ideal circumstance. However, the inconsistent part hidden in each view, including noises and specific elements, may affect the quality of mapping between labels and feature representations. Meanwhile, ignoring the specific part might lead to a suboptimal result, as each label is supposed to possess specific characteristics of
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Wang, Ziquan, Mingxuan Xia, Xiangyu Ren, et al. "Multi-Instance Multi-Label Classification from Crowdsourced Labels." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 20 (2025): 21438–46. https://doi.org/10.1609/aaai.v39i20.35445.

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Multi-instance multi-label classification (MIML) is a fundamental task in machine learning, where each data sample comprises a bag containing several instances and multiple binary labels. Despite its wide applications, the data collection process involves matching multiple instances and labels, typically resulting in high annotation costs. In this paper, we study a novel yet practical crowdsourced multi-instance multi-label classification (CMIML) setup, where labels are collected from multiple crowd sources. To address this problem, we first propose a novel data generation process for CMIML, i
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Sun, Kai-Wei, Chong Ho Lee, and Xiao-Feng Xie. "MLHN: A Hypernetwork Model for Multi-Label Classification." International Journal of Pattern Recognition and Artificial Intelligence 29, no. 06 (2015): 1550020. http://dx.doi.org/10.1142/s0218001415500202.

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Multi-label classification has attracted significant attentions in machine learning. In multi-label classification, exploiting correlations among labels is an essential but nontrivial task. First, labels may be correlated in various degrees. Second, the scalability may suffer from the large number of labels, because the number of combinations among labels grows exponentially as the number of labels increases. In this paper, a multi-label hypernetwork (MLHN) is proposed to deal with these problems. By extending the traditional hypernetwork model, MLHN can represent arbitrary order correlations
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Guo, Hai-Feng, Lixin Han, Shoubao Su, and Zhou-Bao Sun. "Deep Multi-Instance Multi-Label Learning for Image Annotation." International Journal of Pattern Recognition and Artificial Intelligence 32, no. 03 (2017): 1859005. http://dx.doi.org/10.1142/s021800141859005x.

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Multi-Instance Multi-Label learning (MIML) is a popular framework for supervised classification where an example is described by multiple instances and associated with multiple labels. Previous MIML approaches have focused on predicting labels for instances. The idea of tackling the problem is to identify its equivalence in the traditional supervised learning framework. Motivated by the recent advancement in deep learning, in this paper, we still consider the problem of predicting labels and attempt to model deep learning in MIML learning framework. The proposed approach enables us to train de
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Xing, Yuying, Guoxian Yu, Carlotta Domeniconi, Jun Wang, Zili Zhang, and Maozu Guo. "Multi-View Multi-Instance Multi-Label Learning Based on Collaborative Matrix Factorization." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 5508–15. http://dx.doi.org/10.1609/aaai.v33i01.33015508.

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Multi-view Multi-instance Multi-label Learning (M3L) deals with complex objects encompassing diverse instances, represented with different feature views, and annotated with multiple labels. Existing M3L solutions only partially explore the inter or intra relations between objects (or bags), instances, and labels, which can convey important contextual information for M3L. As such, they may have a compromised performance.\
 In this paper, we propose a collaborative matrix factorization based solution called M3Lcmf. M3Lcmf first uses a heterogeneous network composed of nodes of bags, instanc
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Chen, Tianshui, Tao Pu, Hefeng Wu, Yuan Xie, and Liang Lin. "Structured Semantic Transfer for Multi-Label Recognition with Partial Labels." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 1 (2022): 339–46. http://dx.doi.org/10.1609/aaai.v36i1.19910.

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Multi-label image recognition is a fundamental yet practical task because real-world images inherently possess multiple semantic labels. However, it is difficult to collect large-scale multi-label annotations due to the complexity of both the input images and output label spaces. To reduce the annotation cost, we propose a structured semantic transfer (SST) framework that enables training multi-label recognition models with partial labels, i.e., merely some labels are known while other labels are missing (also called unknown labels) per image. The framework consists of two complementary transf
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Li, Lei, Yuqi Chu, Guanfeng Liu, and Xindong Wu. "Multi-Objective Optimization-Based Networked Multi-Label Active Learning." Journal of Database Management 30, no. 2 (2019): 1–26. http://dx.doi.org/10.4018/jdm.2019040101.

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Along with the fast development of network applications, network research has attracted more and more attention, where one of the most important research directions is networked multi-label classification. Based on it, unknown labels of nodes can be inferred by known labels of nodes in the neighborhood. As both the scale and complexity of networks are increasing, the problems of previously neglected system overhead are turning more and more seriously. In this article, a novel multi-objective optimization-based networked multi-label seed node selection algorithm (named as MOSS) is proposed to i
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Tan, Z. M., J. Y. Liu, Q. Li, D. Y. Wang, and C. Y. Wang. "An approach to error label discrimination based on joint clustering." Journal of Physics: Conference Series 2294, no. 1 (2022): 012018. http://dx.doi.org/10.1088/1742-6596/2294/1/012018.

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Abstract Inaccurate multi-label learning aims at dealing with multi-label data with wrong labels. Error labels in data sets usually result in cognitive bias for objects. To discriminate and correct wrong labels is a significant issue in multi-label learning. In this paper, a joint discrimination model based on fuzzy C-means (FCM) and possible C-means (PCM) is proposed to find wrong labels in data sets. In this model, the connection between samples and their labels is analyzed based on the assumption of consistence between samples and their labels. Samples and labels are clustered by considerin
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Huang, Jun, Linchuan Xu, Kun Qian, Jing Wang, and Kenji Yamanishi. "Multi-label learning with missing and completely unobserved labels." Data Mining and Knowledge Discovery 35, no. 3 (2021): 1061–86. http://dx.doi.org/10.1007/s10618-021-00743-x.

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AbstractMulti-label learning deals with data examples which are associated with multiple class labels simultaneously. Despite the success of existing approaches to multi-label learning, there is still a problem neglected by researchers, i.e., not only are some of the values of observed labels missing, but also some of the labels are completely unobserved for the training data. We refer to the problem as multi-label learning with missing and completely unobserved labels, and argue that it is necessary to discover these completely unobserved labels in order to mine useful knowledge and make a de
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Rozprawy doktorskie na temat "Multi-labels"

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Seddighian, Pegah. "Optical Packet Switching using Multi-Wavelength Labels." Doctoral thesis, Université Laval, 2008. http://www.theses.ulaval.ca/2008/25239/25239.pdf.

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VanValkenburg, Schuyler. "Defying Labels: Richmond NOW’s Multi-Generational Dynamism." VCU Scholars Compass, 2010. http://scholarscompass.vcu.edu/etd/2203.

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In the late 1960s a group of women became interested in forming a chapter of the National Organization for Women (NOW) in Richmond. These women, led by Zelda Nordlinger and Holt Carlton, followed a pragmatic, big-tent approach to women’s activism. This ideological and tactical openness defies traditional historical labels as these women fluidly moved through organizations and tactics in order to gain a stronger local following. Richmond’s NOW chapter, while staying attuned to the national organization’s platform, remained relatively autonomous and parochial in its tactics and pursuits. Fur
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Smida, F. A. "Photochemical harpoons : covalent labels for multi-protein complexes." Thesis, Nottingham Trent University, 2013. http://irep.ntu.ac.uk/id/eprint/69/.

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The identification of the biomolecular interaction partners of small bioactive molecules is a fundamental problem in drug discovery and cell biology. This thesis describes the development of fluorescent chemical probes to identify the biomolecular targets of the known organophosphate toxin, phenyl saligenin phosphate (PSP), and the cardioprotective agent diazoxide. PSP is an organophosphate toxin that irreversibly inhibits hydrolase enzymes such as trypsin and chymotrypsin along with the common organophosphate target acetylcholine esterase. PSP is also suspected of affecting many other cell fu
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Arens, Maxime. "Apprentissage actif multi-labels pour des architectures transformers." Electronic Thesis or Diss., Université de Toulouse (2023-....), 2024. http://www.theses.fr/2024TLSES052.

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L'annotation des données est cruciale pour l'apprentissage automatique, notamment dans les domaines techniques, où la qualité et la quantité des données annotées affectent significativement l'efficacité des modèles entraînés. L'utilisation de personnel humain est coûteuse, surtout lors de l'annotation pour la classification multi-labels, les instances pouvant être associées à plusieurs labels. L'apprentissage actif (AA) vise à réduire les coûts d'annotation en sélectionnant intelligemment des instances pour l'annotation, plutôt que de les annoter de manière aléatoire. L'attention récente porté
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Li, Xile. "Real-time Multi-face Tracking with Labels based on Convolutional Neural Networks." Thesis, Université d'Ottawa / University of Ottawa, 2017. http://hdl.handle.net/10393/36707.

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This thesis presents a real-time multi-face tracking system, which is able to track multiple faces for live videos, broadcast, real-time conference recording, etc. The real-time output is one of the most significant advantages. Our proposed tracking system is comprised of three parts: face detection, feature extraction and tracking. We deploy a three-layer Convolutional Neural Network (CNN) to detect a face, a one-layer CNN to extract the features of a detected face and a shallow network for face tracking based on the extracted feature maps of the face. The performance of our multi-face
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Kraus, Vivien. "Apprentissage semi-supervisé pour la régression multi-labels : application à l’annotation automatique de pneumatiques." Thesis, Lyon, 2021. https://tel.archives-ouvertes.fr/tel-03789608.

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Avec l’avènement et le développement rapide des technologies numériques, les données sont devenues à la fois un bien précieux et très abondant. Cependant, avec une telle profusion, se posent des questions relatives à la qualité et l’étiquetage de ces données. En effet, à cause de l’augmentation des volumes de données disponibles, alors que le coût de l’étiquetage par des experts humains reste très important, il est de plus en plus nécessaire de pouvoir renforcer l’apprentissage semi-supervisé grâce l’exploitation des données nonlabellisées. Ce problème est d’autant plus marqué dans le cas de l
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Laghmari, Khalil. "Classification multi-labels graduée : découverte des relations entre les labels, et adaptation à la reconnaissance des odeurs et au contexte big data des systèmes de recommandation." Thesis, Sorbonne université, 2018. http://www.theses.fr/2018SORUS032/document.

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En classification multi-labels graduée (CMLG), chaque instance est associée à un ensemble de labels avec des degrés d’association gradués. Par exemple, une même molécule odorante peut être associée à une odeur forte ‘musquée’, une odeur modérée ‘animale’, et une odeur faible ‘herbacée’. L’objectif est d’apprendre un modèle permettant de prédire l’ensemble gradué de labels associé à une instance à partir de ses variables descriptives. Par exemple, prédire l’ensemble gradué d’odeurs à partir de la masse moléculaire, du nombre de liaisons doubles, et de la structure de la molécule. Un autre domai
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Laghmari, Khalil. "Classification multi-labels graduée : découverte des relations entre les labels, et adaptation à la reconnaissance des odeurs et au contexte big data des systèmes de recommandation." Electronic Thesis or Diss., Sorbonne université, 2018. http://www.theses.fr/2018SORUS032.

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En classification multi-labels graduée (CMLG), chaque instance est associée à un ensemble de labels avec des degrés d’association gradués. Par exemple, une même molécule odorante peut être associée à une odeur forte ‘musquée’, une odeur modérée ‘animale’, et une odeur faible ‘herbacée’. L’objectif est d’apprendre un modèle permettant de prédire l’ensemble gradué de labels associé à une instance à partir de ses variables descriptives. Par exemple, prédire l’ensemble gradué d’odeurs à partir de la masse moléculaire, du nombre de liaisons doubles, et de la structure de la molécule. Un autre domai
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Chazelle, Thomas. "Influence sociale sur la représentation corporelle : Approche expérimentale de l'effet des médias et des labels de poids sur des jugements de corpulence." Electronic Thesis or Diss., Université Grenoble Alpes, 2023. http://www.theses.fr/2023GRALS063.

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La représentation corporelle est l’ensemble des fonctions cognitives permettant le suivi de l’état du corps. Elle est impliquée dans des situations diverses, comme la perception des dimensions physiques du corps, l’action, ou encore la génération d’attitudes à propos du corps. Pour réaliser ces fonctions, elle se base de manière flexible sur un ensemble d’informations sensorimotrices, ainsi que sur les croyances, attentes et émotions de l’individu. Parmi les sources d’informations disponibles à propos du corps, l’influence sociale peut être un facteur de risque, de maintien, et de sévérité des
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Benkarim, Mohamed Oualid. "Multi-atlas segmentation and analysis of the fetal brain in ventriculomegaly." Doctoral thesis, Universitat Pompeu Fabra, 2018. http://hdl.handle.net/10803/663747.

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Nowadays, imaging of the human brain is vastly used in clinical settings and by the neuroscientific research community. There is an ever-increasing demand for novel biomedical image analysis approaches and tools to study the brain from its early intrauterine stage through adolescence to adulthood. The intrauterine period, in particular, is a crucial stage for the study of early neurodevelopmental processes. The idiosyncratic nature of the fetal brain poses numerous challenges and asks for the development of new techniques that take into consideration the peculiarities of in utero neurodevelopm
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Książki na temat "Multi-labels"

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Stein, Torsten. Legal limits of the fight against tobacco consumption in multi-level governance. Nomos, 2011.

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Sutton, Allan. Directory of American Disc Record Brands and Manufacturers, 1891-1943. Greenwood, 1994. http://dx.doi.org/10.5040/9798400640827.

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The Directoryprovides an indepth examination of the growth of the American disc record industry from the introduction of Berliner's disc Gramophone through the Petrillo recording ban. It examines the histories of more than 330 labels and their manufacturers, chronicalling the growth of the disc record from a crude toy in the 1890s to a multi-million dollar industry in the early 1940s. In this process, the Directory shows how power eventually came to rest in the hands of several major manufacturers. Taken largely from original source material,The Directoryreveals master sources, master leasing
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Części książek na temat "Multi-labels"

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Protaziuk, Grzegorz, Marcin Kaczyński, and Robert Bembenik. "Automatic Translation of Multi-word Labels." In Studies in Big Data. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-30315-4_9.

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Ai, Qing, Ji Zhao, and Yuping Qin. "A Novel Multi-Labels Classification Algorithm." In Lecture Notes in Electrical Engineering. Springer London, 2013. http://dx.doi.org/10.1007/978-1-4471-4856-2_68.

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Stachniss, Cyrill. "Multi-Robot Exploration Using Semantic Place Labels." In Springer Tracts in Advanced Robotics. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01097-2_5.

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Azarbonyad, Hosein, and Maarten Marx. "How Many Labels? Determining the Number of Labels in Multi-Label Text Classification." In Lecture Notes in Computer Science. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-28577-7_11.

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Xu, Qian, Pengfei Zhu, Qinghua Hu, and Changqing Zhang. "Robust Multi-label Feature Selection with Missing Labels." In Communications in Computer and Information Science. Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-3002-4_61.

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Mieszkowicz-Rolka, Alicja, and Leszek Rolka. "Fuzzy Linguistic Labels in Multi-expert Decision Making." In Theory and Practice of Natural Computing. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-71069-3_10.

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Wang, Qing, and Liang Zhang. "Ensemble Learning Based on Multi-Task Class Labels." In Advances in Knowledge Discovery and Data Mining. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13672-6_44.

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Chen, Zhenghan, Changzeng Fu, and Xunzhu Tang. "Multi-domain Fake News Detection with Fuzzy Labels." In Database Systems for Advanced Applications. DASFAA 2023 International Workshops. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-35415-1_23.

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Wang, Lun, Wentao Xiao, and Shan Ye. "Dynamic Multi-label Learning with Multiple New Labels." In Lecture Notes in Computer Science. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-34113-8_35.

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Kagias, Antonios, and Georgios Evangelidis. "Classification on Multi-label Data with Ordered Labels." In Communications in Computer and Information Science. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-93598-5_21.

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Streszczenia konferencji na temat "Multi-labels"

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Byun, Min Sik, Malcolm Yoke Hean Low, and David Weidong Lin. "A Multi-Modal AI Inspection System for Laser Labels." In TENCON 2024 - 2024 IEEE Region 10 Conference (TENCON). IEEE, 2024. https://doi.org/10.1109/tencon61640.2024.10902911.

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Xing, Xin, Zhexiao Xiong, Abby Stylianou, Srikumar Sastry, Liyu Gong, and Nathan Jacobs. "Vision-Language Pseudo-Labels for Single-Positive Multi-Label Learning." In 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE, 2024. http://dx.doi.org/10.1109/cvprw63382.2024.00776.

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Wei, Tong, and Yu-Feng Li. "Does Tail Label Help for Large-Scale Multi-Label Learning." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/395.

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Large-scale multi-label learning annotates relevant labels for unseen data from a huge number of candidate labels. It is well known that in large-scale multi-label learning, labels exhibit a long tail distribution in which a significant fraction of labels are tail labels. Nonetheless, how tail labels make impact on the performance metrics in large-scale multi-label learning was not explicitly quantified. In this paper, we disclose that whatever labels are randomly missing or misclassified, tail labels impact much less than common labels in terms of commonly used performance metrics (Top-$k$ pr
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Wu, Baoyuan, Zhilei Liu, Shangfei Wang, Bao-Gang Hu, and Qiang Ji. "Multi-label Learning with Missing Labels." In 2014 22nd International Conference on Pattern Recognition (ICPR). IEEE, 2014. http://dx.doi.org/10.1109/icpr.2014.343.

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Read, Jesse, Antti Puurula, and Albert Bifet. "Multi-label Classification with Meta-Labels." In 2014 IEEE International Conference on Data Mining (ICDM). IEEE, 2014. http://dx.doi.org/10.1109/icdm.2014.38.

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Li Shuguang and Xin Xiao. "Multi-multiway cuts with edge labels." In Education (ICCSE). IEEE, 2009. http://dx.doi.org/10.1109/iccse.2009.5228230.

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Liu, Wenqiang, Yang Li, Jiabao Wang, Zhuang Miao, and Hangping Qiu. "Multi-object Tracking with Noisy Labels." In 2022 5th International Conference on Pattern Recognition and Artificial Intelligence (PRAI). IEEE, 2022. http://dx.doi.org/10.1109/prai55851.2022.9904177.

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Guo, Huaping, and Ming Fan. "Multi-Label Classification via Manipulating Labels." In 2nd International Conference on Computer Science and Electronics Engineering (ICCSEE 2013). Atlantis Press, 2013. http://dx.doi.org/10.2991/iccsee.2013.245.

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Wang, Haobo, Weiwei Liu, Yang Zhao, Tianlei Hu, Ke Chen, and Gang Chen. "Learning From Multi-Dimensional Partial Labels." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/407.

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Multi-dimensional classification has attracted huge attention from the community. Though most studies consider fully annotated data, in real practice obtaining fully labeled data in MDC tasks is usually intractable. In this paper, we propose a novel learning paradigm: MultiDimensional Partial Label Learning (MDPL) where the ground-truth labels of each instance are concealed in multiple candidate label sets. We first introduce the partial hamming loss for MDPL that incurs a large loss if the predicted labels are not in candidate label sets, and provide an empirical risk minimization (ERM) frame
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Yun, Sangdoo, Seong Joon Oh, Byeongho Heo, Dongyoon Han, Junsuk Choe, and Sanghyuk Chun. "Re-labeling ImageNet: from Single to Multi-Labels, from Global to Localized Labels." In 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2021. http://dx.doi.org/10.1109/cvpr46437.2021.00237.

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Raporty organizacyjne na temat "Multi-labels"

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Li, Y., D. Eastlake, W. Hao, H. Chen, and S. Chatterjee. Transparent Interconnection of Lots of Links (TRILL): Using Data Labels for Tree Selection for Multi-Destination Data. RFC Editor, 2016. http://dx.doi.org/10.17487/rfc7968.

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Walizer, Laura, Robert Haehnel, Luke Allen, and Yonghu Wenren. Application of multi-fidelity methods to rotorcraft performance assessment. Engineer Research and Development Center (U.S.), 2024. http://dx.doi.org/10.21079/11681/48474.

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We present a Python-based multi-fidelity tool to estimate rotorcraft performance metrics. We use Gaussian-Process regression (GPR) methods to adaptively build a surrogate model using a small number of high-fidelity CFD points to improve estimates of performance metrics from a medium-fidelity comprehensive analysis model. To include GPR methods in our framework, we used the EmuKit Python package. Our framework adaptively chooses new high-fidelity points to run in regions where the model variance is high. These high-fidelity points are used to update the GPR model; convergence is reached when mo
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