Literatura académica sobre el tema "Multi-labels"
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Artículos de revistas sobre el tema "Multi-labels"
Lee, Seongmin, Hyunsik Jeon y U. Kang. "Multi-EPL: Accurate multi-source domain adaptation". PLOS ONE 16, n.º 8 (5 de agosto de 2021): e0255754. http://dx.doi.org/10.1371/journal.pone.0255754.
Texto completoHao, Pingting, Kunpeng Liu y Wanfu Gao. "Double-Layer Hybrid-Label Identification Feature Selection for Multi-View Multi-Label Learning". Proceedings of the AAAI Conference on Artificial Intelligence 38, n.º 11 (24 de marzo de 2024): 12295–303. http://dx.doi.org/10.1609/aaai.v38i11.29120.
Texto completoSun, Kai-Wei, Chong Ho Lee y Xiao-Feng Xie. "MLHN: A Hypernetwork Model for Multi-Label Classification". International Journal of Pattern Recognition and Artificial Intelligence 29, n.º 06 (12 de agosto de 2015): 1550020. http://dx.doi.org/10.1142/s0218001415500202.
Texto completoGuo, Hai-Feng, Lixin Han, Shoubao Su y Zhou-Bao Sun. "Deep Multi-Instance Multi-Label Learning for Image Annotation". International Journal of Pattern Recognition and Artificial Intelligence 32, n.º 03 (22 de noviembre de 2017): 1859005. http://dx.doi.org/10.1142/s021800141859005x.
Texto completoXing, Yuying, Guoxian Yu, Carlotta Domeniconi, Jun Wang, Zili Zhang y Maozu Guo. "Multi-View Multi-Instance Multi-Label Learning Based on Collaborative Matrix Factorization". Proceedings of the AAAI Conference on Artificial Intelligence 33 (17 de julio de 2019): 5508–15. http://dx.doi.org/10.1609/aaai.v33i01.33015508.
Texto completoLi, Lei, Yuqi Chu, Guanfeng Liu y Xindong Wu. "Multi-Objective Optimization-Based Networked Multi-Label Active Learning". Journal of Database Management 30, n.º 2 (abril de 2019): 1–26. http://dx.doi.org/10.4018/jdm.2019040101.
Texto completoChen, Tianshui, Tao Pu, Hefeng Wu, Yuan Xie y Liang Lin. "Structured Semantic Transfer for Multi-Label Recognition with Partial Labels". Proceedings of the AAAI Conference on Artificial Intelligence 36, n.º 1 (28 de junio de 2022): 339–46. http://dx.doi.org/10.1609/aaai.v36i1.19910.
Texto completoHuang, Jun, Linchuan Xu, Kun Qian, Jing Wang y Kenji Yamanishi. "Multi-label learning with missing and completely unobserved labels". Data Mining and Knowledge Discovery 35, n.º 3 (12 de marzo de 2021): 1061–86. http://dx.doi.org/10.1007/s10618-021-00743-x.
Texto completoChen, Ze-Sen, Xuan Wu, Qing-Guo Chen, Yao Hu y Min-Ling Zhang. "Multi-View Partial Multi-Label Learning with Graph-Based Disambiguation". Proceedings of the AAAI Conference on Artificial Intelligence 34, n.º 04 (3 de abril de 2020): 3553–60. http://dx.doi.org/10.1609/aaai.v34i04.5761.
Texto completoHuang, Jun, Haowei Rui, Guorong Li, Xiwen Qu, Tao Tao y Xiao Zheng. "Multi-Label Learning With Hidden Labels". IEEE Access 8 (2020): 29667–76. http://dx.doi.org/10.1109/access.2020.2972599.
Texto completoTesis sobre el tema "Multi-labels"
Seddighian, Pegah. "Optical Packet Switching using Multi-Wavelength Labels". Doctoral thesis, Université Laval, 2008. http://www.theses.ulaval.ca/2008/25239/25239.pdf.
Texto completoVanValkenburg, Schuyler. "Defying Labels: Richmond NOW’s Multi-Generational Dynamism". VCU Scholars Compass, 2010. http://scholarscompass.vcu.edu/etd/2203.
Texto completoSmida, F. A. "Photochemical harpoons : covalent labels for multi-protein complexes". Thesis, Nottingham Trent University, 2013. http://irep.ntu.ac.uk/id/eprint/69/.
Texto completoArens, Maxime. "Apprentissage actif multi-labels pour des architectures transformers". Electronic Thesis or Diss., Université de Toulouse (2023-....), 2024. http://www.theses.fr/2024TLSES052.
Texto completoData annotation is crucial for machine learning, especially in technical domains, where the quality and quantity of annotated data significantly impact the effectiveness of trained models. Human annotation is costly, particularly for multi-label classification tasks, as instances may be associated with multiple labels. Active Learning (AL) aims to reduce annotation costs by intelligently selecting instances for annotation, rather than annotating randomly. Recent attention on transformers has highlighted the potential of AL in this context. Moreover, the fine-tuning mechanism, where only a few annotated data points are used to train the model for a new task, aligns well with the goal of AL to select the best data for annotation. We investigate the use of AL in the context of transformers for multi-label classification tasks. However, most AL strategies, when applied to these models, lead to excessive computational time, hindering their use in real-time human-machine interaction. To address this issue, we employ faster AL strategies based on uncertainty. First, we focus on applying six different AL strategies to two transformer models. Our work highlights that several uncertainty-based strategies do not outperform random sampling when applied to transformer models. To evaluate if these results stem from a bias in uncertainty-based strategies, we introduce a pre-clustering approach to add diversity to instance selection. Lastly, we tackle the practical challenges of implementing AL in industrial contexts. Particularly, the gap between AL cycles leaves idle time for annotators. To resolve this, we explore alternative instance selection methods aiming to maximize annotation efficiency by seamlessly integrating with the AL process. We start by adapting two existing methods to transformers, using random sampling and outdated AL cycle information, respectively. Then, we propose our innovative method based on instance annotation to rebalance label distribution. Our approach mitigates biases, improves model performance (up to 23% improvement on the F1 score), reduces strategy-dependent disparities (nearly 50% decrease in standard deviation), and decreases label imbalance (30% decrease in the mean imbalance ratio). Our work thus revives the promise of AL by demonstrating that its adapted integration into an annotation project results in improved performance of the final trained model
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.
Texto completoKraus, 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.
Texto completoWith the advent and rapid growth of digital technologies, data has become a precious asset as well as plentiful. However, with such an abundance come issues about data quality and labelling. Because of growing numbers of available data volumes, while human expert labelling is still important, it is more and more necessary to reinforce semi-supervised learning with the exploitation of unlabeled data. This problem is all the more noticeable in the multi-label learning framework, and in particular for regression, where each statistical unit is guided by many different targets, taking the form of numerical scores. This thesis focuses on this fundamental framework. First, we begin by proposing a method for semi-supervised regression, that we challenge through a detailed experimental study. Thanks to this new method, we present a second contribution, more fitted to the multi-label framework. We also show its efficiency with a comparative study on literature data sets. Furthermore, the problem dimension is always a pain point of machine learning, and reducing it sparks the interest of many researchers. Feature selection is one of the major tasks addressing this problem, and we propose to study it here in a complex framework : for semi-supervised, multi-label regression. Finally, an experimental validation is proposed on a real problem about automatic annotation of tires, to tackle the needs expressed by the industrial partner of this thesis
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.
Texto completoIn graded multi-label classification (GMLC), each instance is associated to a set of labels with graded membership degrees. For example, the same odorous molecule may be associated to a strong 'musky' odor, a moderate 'animal' odor, and a weak 'grassy' odor. The goal is to learn a model to predict the graded set of labels associated to an instance from its descriptive variables. For example, predict the graduated set of odors from the molecular weight, the number of double bonds, and the structure of the molecule. Another interesting area of the GMLC is recommendation systems. In fact, users' assessments of items (products, services, books, films, etc.) are first collected in the form of GML data (using the one-to-five star rating). These data are then used to recommend to each user items that are most likely to interest him. In this thesis, an in-depth theoretical study of the GMLC allows to highlight the limits of existing approaches, and to introduce a set of new approaches bringing improvements evaluated experimentally on real data. The main point of the new proposed approaches is the exploitation of relations between labels. For example, a molecule with a strong 'musky' odor often has a weak or moderate 'animal' odor. This thesis also proposes new approaches adapted to the case of odorous molecules and to the case of large volumes of data collected in the context of recommendation systems
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.
Texto completoIn graded multi-label classification (GMLC), each instance is associated to a set of labels with graded membership degrees. For example, the same odorous molecule may be associated to a strong 'musky' odor, a moderate 'animal' odor, and a weak 'grassy' odor. The goal is to learn a model to predict the graded set of labels associated to an instance from its descriptive variables. For example, predict the graduated set of odors from the molecular weight, the number of double bonds, and the structure of the molecule. Another interesting area of the GMLC is recommendation systems. In fact, users' assessments of items (products, services, books, films, etc.) are first collected in the form of GML data (using the one-to-five star rating). These data are then used to recommend to each user items that are most likely to interest him. In this thesis, an in-depth theoretical study of the GMLC allows to highlight the limits of existing approaches, and to introduce a set of new approaches bringing improvements evaluated experimentally on real data. The main point of the new proposed approaches is the exploitation of relations between labels. For example, a molecule with a strong 'musky' odor often has a weak or moderate 'animal' odor. This thesis also proposes new approaches adapted to the case of odorous molecules and to the case of large volumes of data collected in the context of recommendation systems
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.
Texto completoBody representation is the set of cognitive functions that track the state of the body. It is involved in a variety of situations, such as the perception of the physical dimensions of the body, action, and the generation of attitudes towards the body. To perform these functions, it relies on the flexible use of a range of sensorimotor information, as well as on the individual's beliefs, expectations and emotions. Among the sources of information available about the body, social influence can be a risk, maintenance, and severity factor in body image distortions. However, while social influence on the attitudinal aspects of body representation is well established, there is little experimental evidence of such influence on its perceptual aspects. The aim of this thesis is to study the integration of social information into the perceptual dimension of the representation of body size. To this end, we conducted a series of experiments with young women, a demographic that is particularly prone to distortions of body representation. A first axis focuses on interpersonal influence by testing the effect of weight labels on perceptual judgments. To investigate their informational influence, we manipulated the reliability of multiple cues to study how they were combined. Our results indicate that weight labels have a limited influence on judgments of body size. A second axis focuses on another type of social influence, media influence. Visual overexposure to specific body types is associated with body dissatisfaction, and could help explain the perceptual and attitudinal distortions of body representation. In this context, visual adaptation to bodies could explain how prolonged exposure to thin bodies can lead to an overestimation of one's own body size. We tested some of the hypotheses of this adaptation theory of body image distortion. These experiments highlight some limitations of the adaptation account; in particular, it is uncertain whether adaptation effects can influence the representation that individuals have of their own bodies. In conclusion, our results suggest that the perceptual dimension of the representation of body size may be resistant to some types of interpersonal and media social influence
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.
Texto completoEn la actualidad, las imagenes del cerebro humano son ampliamente utilizadas en entornos clıınicos y por la comunidad neurocientııfica. Existe una demanda, cada vez mayor, de herramientas y enfoques de analisis de imagenes biomédicas novedosos para estudiar el cerebro desde su temprana etapa intrauterina hasta la adolescencia y la edad adulta. El periodo intrauterino, en particular, es una etapa crucial para el estudio de los procesos iniciales del neurodesarrollo. La naturaleza idiosincrasica del cerebro fetal plantea numerosos desafııos y requiere el desarrollo de nuevas técnicas que contemplen las peculiaridades del neurodesarrollo fetal. Aunque todavııa esta en su infancia, las técnicas de analisis de imagenes médicas estan llegando progresivamente al estudio de los cerebros fetales. El objetivo de esta tesis es desarrollar métodos automaticos de segmentación que puedan aplicarse a cerebros en distintas etapas de la vida, incluyendo el periodo gestacional, e investigar el desarrollo del cerebro fetal con ventriculomegalia.
Libros sobre el tema "Multi-labels"
Stein, Torsten. Legal limits of the fight against tobacco consumption in multi-level governance. Baden-Baden: Nomos, 2011.
Buscar texto completoSutton, Allan. Directory of American Disc Record Brands and Manufacturers, 1891-1943. Greenwood, 1994. http://dx.doi.org/10.5040/9798400640827.
Texto completoCapítulos de libros sobre el tema "Multi-labels"
Protaziuk, Grzegorz, Marcin Kaczyński y Robert Bembenik. "Automatic Translation of Multi-word Labels". En Studies in Big Data, 99–109. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-30315-4_9.
Texto completoAi, Qing, Ji Zhao y Yuping Qin. "A Novel Multi-Labels Classification Algorithm". En Lecture Notes in Electrical Engineering, 571–77. London: Springer London, 2013. http://dx.doi.org/10.1007/978-1-4471-4856-2_68.
Texto completoStachniss, Cyrill. "Multi-Robot Exploration Using Semantic Place Labels". En Springer Tracts in Advanced Robotics, 73–90. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01097-2_5.
Texto completoAzarbonyad, Hosein y Maarten Marx. "How Many Labels? Determining the Number of Labels in Multi-Label Text Classification". En Lecture Notes in Computer Science, 156–63. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-28577-7_11.
Texto completoXu, Qian, Pengfei Zhu, Qinghua Hu y Changqing Zhang. "Robust Multi-label Feature Selection with Missing Labels". En Communications in Computer and Information Science, 752–65. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-3002-4_61.
Texto completoMieszkowicz-Rolka, Alicja y Leszek Rolka. "Fuzzy Linguistic Labels in Multi-expert Decision Making". En Theory and Practice of Natural Computing, 126–36. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-71069-3_10.
Texto completoWang, Qing y Liang Zhang. "Ensemble Learning Based on Multi-Task Class Labels". En Advances in Knowledge Discovery and Data Mining, 464–75. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13672-6_44.
Texto completoWang, Lun, Wentao Xiao y Shan Ye. "Dynamic Multi-label Learning with Multiple New Labels". En Lecture Notes in Computer Science, 421–31. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-34113-8_35.
Texto completoChen, Zhenghan, Changzeng Fu y Xunzhu Tang. "Multi-domain Fake News Detection with Fuzzy Labels". En Database Systems for Advanced Applications. DASFAA 2023 International Workshops, 331–43. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-35415-1_23.
Texto completoRen, Weijieying, Lei Zhang, Bo Jiang, Zhefeng Wang, Guangming Guo y Guiquan Liu. "Robust Mapping Learning for Multi-view Multi-label Classification with Missing Labels". En Knowledge Science, Engineering and Management, 543–51. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-63558-3_46.
Texto completoActas de conferencias sobre el tema "Multi-labels"
Wu, Baoyuan, Zhilei Liu, Shangfei Wang, Bao-Gang Hu y Qiang Ji. "Multi-label Learning with Missing Labels". En 2014 22nd International Conference on Pattern Recognition (ICPR). IEEE, 2014. http://dx.doi.org/10.1109/icpr.2014.343.
Texto completoRead, Jesse, Antti Puurula y Albert Bifet. "Multi-label Classification with Meta-Labels". En 2014 IEEE International Conference on Data Mining (ICDM). IEEE, 2014. http://dx.doi.org/10.1109/icdm.2014.38.
Texto completoLi Shuguang y Xin Xiao. "Multi-multiway cuts with edge labels". En Education (ICCSE). IEEE, 2009. http://dx.doi.org/10.1109/iccse.2009.5228230.
Texto completoLiu, Wenqiang, Yang Li, Jiabao Wang, Zhuang Miao y Hangping Qiu. "Multi-object Tracking with Noisy Labels". En 2022 5th International Conference on Pattern Recognition and Artificial Intelligence (PRAI). IEEE, 2022. http://dx.doi.org/10.1109/prai55851.2022.9904177.
Texto completoGuo, Huaping y Ming Fan. "Multi-Label Classification via Manipulating Labels". En 2nd International Conference on Computer Science and Electronics Engineering (ICCSEE 2013). Paris, France: Atlantis Press, 2013. http://dx.doi.org/10.2991/iccsee.2013.245.
Texto completoWang, Haobo, Weiwei Liu, Yang Zhao, Tianlei Hu, Ke Chen y Gang Chen. "Learning From Multi-Dimensional Partial Labels". En Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/407.
Texto completoWei, Tong y Yu-Feng Li. "Does Tail Label Help for Large-Scale Multi-Label Learning". En Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/395.
Texto completoYun, Sangdoo, Seong Joon Oh, Byeongho Heo, Dongyoon Han, Junsuk Choe y Sanghyuk Chun. "Re-labeling ImageNet: from Single to Multi-Labels, from Global to Localized Labels". En 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2021. http://dx.doi.org/10.1109/cvpr46437.2021.00237.
Texto completoAgrawal, Rahul, Archit Gupta, Yashoteja Prabhu y Manik Varma. "Multi-label learning with millions of labels". En the 22nd international conference. New York, New York, USA: ACM Press, 2013. http://dx.doi.org/10.1145/2488388.2488391.
Texto completoWenrong Zeng, Xuewen Chen y Hong Cheng. "Pseudo labels for imbalanced multi-label learning". En 2014 International Conference on Data Science and Advanced Analytics (DSAA). IEEE, 2014. http://dx.doi.org/10.1109/dsaa.2014.7058047.
Texto completoInformes sobre el tema "Multi-labels"
Li, Y., D. Eastlake, W. Hao, H. Chen y S. Chatterjee. Transparent Interconnection of Lots of Links (TRILL): Using Data Labels for Tree Selection for Multi-Destination Data. RFC Editor, agosto de 2016. http://dx.doi.org/10.17487/rfc7968.
Texto completoWalizer, Laura, Robert Haehnel, Luke Allen y Yonghu Wenren. Application of multi-fidelity methods to rotorcraft performance assessment. Engineer Research and Development Center (U.S.), mayo de 2024. http://dx.doi.org/10.21079/11681/48474.
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