Academic literature on the topic 'Multi-labels'
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Journal articles on the topic "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 extraction with a single extractor. In this paper, we propose Multi-EPL, a novel method for MSDA. Multi-EPL exploits label-wise moment matching to align the conditional distributions of the features for the labels, uses pseudolabels for the unavailable target labels, and introduces an ensemble of multiple feature extractors for accurate domain adaptation. Extensive experiments show that Multi-EPL provides the state-of-the-art performance for MSDA tasks in both image domains and text domains, improving the accuracy by up to 13.20%.
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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 its own. To deal with the double problems in multi-view multi-label feature selection, we propose a unified loss function which is a totally splitting structure for observed labels as hybrid labels that is, common labels, view-to-all specific labels and noisy labels, and the view-to-all specific labels further splits into several specific labels of each view. The proposed method simultaneously considers the consistency and complementarity of different views. Through exploring the feature weights of hybrid labels, the mapping relationships between labels and features can be established sequentially based on their attributes. Additionally, the interrelatedness among hybrid labels is also investigated and injected into the function. Specific to the specific labels of each view, we construct the novel regularization paradigm incorporating logic operations. Finally, the convergence of the result is proved after applying the multiplicative update rules. Experiments on six datasets demonstrate the effectiveness and superiority of our method compared with the state-of-the-art methods.
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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.e., cross-label transition, where cross-label annotation error is more likely to appear rather than previous single-label transition assumption, due to the inherent similarity of localized instances from different classes. Then, we formally define the cross-label transition by cross-label transition matrices which are dependent across classes. Subsequently, we establish the first unbiased risk estimator for CMIML and further improve it through aggregation techniques, along with a rigorous generalization error bound. We also provide a practical implementation of cross-label transition matrix estimation. Comprehensive experiments on six benchmark datasets under various scenarios demonstrate that our algorithm outperforms the baselines by a large margin, validating its effectiveness in handling the CMIML problem.
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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 among labels. The classification model of MLHN is simple and the computational complexity of MLHN is linear with respect to the number of labels, which contribute to the good scalability of MLHN. We perform experiments on a variety of datasets. The results illustrate that the proposed MLHN achieves competitive performances against state-of-the-art multi-label classification algorithms in terms of both effectiveness and scalability with respect to the number of labels.
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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 deep convolutional neural network with images from social networks where images are well labeled, even labeled with several labels or uncorrelated labels. Experiments on real-world datasets demonstrate the effectiveness of our proposed approach.
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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, instances, and labels, to encode different types of relations via multiple relational data matrices. To preserve the intrinsic structure of the data matrices, M3Lcmf collaboratively factorizes them into low-rank matrices, explores the latent relationships between bags, instances, and labels, and selectively merges the data matrices. An aggregation scheme is further introduced to aggregate the instance-level labels into bag-level and to guide the factorization. An empirical study on benchmark datasets show that M3Lcmf outperforms other related competitive solutions both in the instance-level and bag-level prediction.
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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 transfer modules that explore within-image and cross-image semantic correlations to transfer knowledge of known labels to generate pseudo labels for unknown labels. Specifically, an intra-image semantic transfer module learns image-specific label co-occurrence matrix and maps the known labels to complement unknown labels based on this matrix. Meanwhile, a cross-image transfer module learns category-specific feature similarities and helps complement unknown labels with high similarities. Finally, both known and generated labels are used to train the multi-label recognition models. Extensive experiments on the Microsoft COCO, Visual Genome and Pascal VOC datasets show that the proposed SST framework obtains superior performance over current state-of-the-art algorithms. Codes are available at https://github.com/HCPLab-SYSU/HCP-MLR-PL.
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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 improve both the prediction accuracy for unknown labels of nodes from labels of seed nodes during classification and the system overhead for mining the labels of seed nodes with third parties before classification. Compared with other algorithms on several real networked data sets, MOSS algorithm not only greatly reduces the system overhead before classification but also improves the prediction accuracy during classification.
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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 considering this connection in the joint FCM-PCM clustering model. An inconsistence measure between a sample and its label is established to recognize wrong labels. A series of simulated experiments are comparatively implemented on several real multi-label data sets and experimental results show superior performance of the proposed model in comparison with two state of the art methods of mislabeling correction.
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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 deeper understanding of what is behind the data. In this paper, we propose a new approach named MCUL to solve multi-label learning with Missing and Completely Unobserved Labels. We try to discover the unobserved labels of a multi-label data set with a clustering based regularization term and describe the semantic meanings of them based on the label-specific features learned by MCUL, and overcome the problem of missing labels by exploiting label correlations. The proposed method MCUL can predict both the observed and newly discovered labels simultaneously for unseen data examples. Experimental results validated over ten benchmark datasets demonstrate that the proposed method can outperform other state-of-the-art approaches on observed labels and obtain an acceptable performance on the new discovered labels as well.
Dissertations / Theses on the topic "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. Further, Richmond NOW showed a marked change around 1974 with an influx of newer women into the organization. The Equal Rights Amendment (ERA) struggle provided the local movement with new prominence. With the interjection of new blood the chapter saw a shift in its tactics and policy. The newer cohort of women maintained a belief in a pragmatic, big-tent approach; however, they interpreted it differently. The chapter became more procedural and organizationally based. It also narrowed its focus and tactics, seeing the first generation’s free-wheeling style as a hindrance to organizational success. The different political experiences of these two cohorts led to different visions of Richmond’s NOW chapter.
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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 functions and may interact with a large number of cellular proteins. In this work phenyl saligenin phosphate has been synthesised and its inhibitory effect on the action of transglutaminase 2 (TGase2) demonstrated. Analogues of PSP containing an attached dansyl amide fluorescent group have been prepared and incubated with purified enzymes trypsin, chymotrypsin and TGase2. SDS-PAGE analysis demonstrates effective fluorescent labelling and a covalent interaction between the toxin analogue and the enzymes. The KATP channel opener, diazoxide displays marked cardioprotective effects and is reported to bind to mitochondrial KATP channels. However, the molecular structure of these channels is still largely unknown. This thesis describes the design and the synthesis of a chemical tool to covalently attach fluorescently labels to the proteins which will bind diazoxide. Chemical tools for fluorescent labelling of diazoxide targeted proteins have been prepared. Each consists of a photochemically activated reactive ‘barb’ and coupled fluorescent component linked to modified diazoxide bait. In developing these molecules, a range of functionalised diazoxide bait components were prepared and tested for retained biological activity compared to the parent compound. Two active analogues were linked to either benzophenone or diazirine (photoreactive) and dansyl amide (fluorescent) components. The non-specific photochemical reactivity of these labelling compounds with bovine serine albumin was established. The incubation and photolysis with mitochondrial extracts showed selective photo labelling of only three biomolecular components. The identification of these biomolecules is the subject of on-going investigation.
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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 portée aux transformers a mis en lumière le potentiel de l'AA dans ce contexte. De plus, le mécanisme de fine-tuning, où seules quelques données annotées sont utilisées pour entraîner le modèle sur une nouvelle tâche, est parfaitement en accord avec l'objectif de l'AA de sélection des meilleures données à annoter. Nous étudions donc l'utilisation de l'AA dans le contexte des transformers pour la tâche de classification multi-labels. Hors, la plupart des stratégies AA, lorsqu'elles sont appliquées à ces modèles, conduisent à des temps de calcul excessifs, ce qui empêche leurs utilisations au cours d'une interaction homme-machine en temps réel. Afin de pallier ce problème, nous utilisons des stratégies d'AA plus rapides, basées sur l'incertitude. D'abord, nous mettons l'accent sur l'application de six stratégies d'AA différentes sur deux modèles transformers. Nos travaux mettent en évidence qu'un certain nombre de stratégies basées sur l'incertitude ne surpassent pas l'échantillonnage aléatoire lorsqu'elles sont appliquées aux modèles transformers. Afin d'évaluer si ces résultats sont dûs à un biais des stratégies basées sur l'incertitude, une approche de pré-clustering est introduite pour ajouter de la diversité dans la sélection des instances. Enfin, nous nous penchons sur les défis pratiques de la mise en œuvre de l'AA dans des contextes industriels. Notamment, l'écart entre les cycles de l'AA laisse du temps inutilisé aux annotateurs. Pour résoudre ce problème, nous étudions des méthodes alternatives de sélection d'instances, visant à maximiser l'efficacité de l'annotation en s'intégrant de manière transparente au processus de l'AA. Nous commençons par adapter deux méthodes existantes aux transformers, en utilisant respectivement un échantillonnage aléatoire et des informations de cycle d'AA périmées. Ensuite, nous proposons notre méthode novatrice basée sur l'annotation des instances pour rééquilibrer la distribution des labels. Notre approche atténue les biais, améliore les performances du modèle (jusqu'à 23% d'amélioration sur le score F1), limite les disparités dépendantes de la stratégie (diminution de près de 50% de l'écart-type) et réduit le déséquilibre des libellés (diminution de 30% du ratio moyen de déséquilibre). Nos travaux ravivent ainsi la promesse de l'AA en montrant que son intégration adaptée dans un projet d'annotation se traduit par une amélioration des performances du modèle final entraîné<br>Data 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
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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 tracking system enables the tracker to run in real-time without any on-line training. This algorithm does not need to change any parameters according to different input video conditions, and the runtime cost will not be affected significantly by an the increase in the number of faces being tracked. In addition, our proposed tracker can overcome most of the generally difficult tracking conditions which include video containing a camera cut, face occlusion, false positive face detection, false negative face detection, e.g. due to faces at the image boundary or faces shown in profile. We use two commonly used metrics to evaluate the performance of our multi-face tracking system demonstrating that our system achieves accurate results. Our multi-face tracker achieves an average runtime cost around 0.035s with GPU acceleration and this runtime cost is close to stable even if the number of tracked faces increases. All the evaluation results and comparisons are tested with four commonly used video data sets.
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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’apprentissage multilabels, et en particulier pour la régression, où chaque unité statistique est guidée par plusieurs cibles différentes, qui prennent la forme de scores numériques. C’est dans ce cadre fondamental, que s’inscrit cette thèse. Tout d’abord, nous commençons par proposer une méthode d’apprentissage pour la régression semi-supervisée, que nous mettons à l’épreuve à travers une étude expérimentale détaillée. Grâce à cette nouvelle méthode, nous présentons une deuxième contribution, plus adaptée au contexte multi-labels. Nous montrons également son efficacité par une étude comparative, sur des jeux de données issues de la littérature. Par ailleurs, la dimensionnalité du problème demeure toujours la difficulté de l’apprentissage automatique, et sa réduction suscite l’intérêt de plusieurs chercheurs dans la communauté. Une des tâches majeures répondant à cette problématique est la sélection de variables, que nous proposons d’étudier ici dans un cadre complexe : semi-supervisé, multi-labels et pour la régression<br>With 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
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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 domaine intéressant de la CMLG est les systèmes de recommandation. En effet, les appréciations des utilisateurs par rapport à des items (produits, services, livres, films, etc) sont d’abord collectées sous forme de données MLG (l’échelle d’une à cinq étoiles est souvent utilisée). Ces données sont ensuite exploitées pour recommander à chaque utilisateur des items qui ont le plus de chance de l’intéresser. Dans cette thèse, une étude théorique approfondie de la CMLG permet de ressortir les limites des approches existantes, et d’assoir un ensemble de nouvelles approches apportant des améliorations évaluées expérimentalement sur des données réelles. Le cœur des nouvelles approches proposées est l’exploitation des relations entre les labels. Par exemple, une molécule ayant une forte odeur ‘musquée’ émet souvent une odeur faible ou modérée ‘animale’. Cette thèse propose également de nouvelles approches adaptées au cas des molécules odorantes et au cas des gros volumes de données collectées dans le cadre des systèmes de recommandation<br>In 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
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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 domaine intéressant de la CMLG est les systèmes de recommandation. En effet, les appréciations des utilisateurs par rapport à des items (produits, services, livres, films, etc) sont d’abord collectées sous forme de données MLG (l’échelle d’une à cinq étoiles est souvent utilisée). Ces données sont ensuite exploitées pour recommander à chaque utilisateur des items qui ont le plus de chance de l’intéresser. Dans cette thèse, une étude théorique approfondie de la CMLG permet de ressortir les limites des approches existantes, et d’assoir un ensemble de nouvelles approches apportant des améliorations évaluées expérimentalement sur des données réelles. Le cœur des nouvelles approches proposées est l’exploitation des relations entre les labels. Par exemple, une molécule ayant une forte odeur ‘musquée’ émet souvent une odeur faible ou modérée ‘animale’. Cette thèse propose également de nouvelles approches adaptées au cas des molécules odorantes et au cas des gros volumes de données collectées dans le cadre des systèmes de recommandation<br>In 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
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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 distorsions de l’image du corps. Pourtant, si l’influence sociale sur les aspects attitudinaux de la représentation corporelle est bien établie, il existe peu d’évidence expérimentale de telles influences sur ses aspects perceptifs. Cette thèse a ainsi pour objectif d’étudier l’intégration d’informations sociales à la dimension perceptive de la représentation de la corpulence. Pour cela, nous avons réalisé une série d’expériences auprès de jeunes femmes, une catégorie de la population particulièrement sujette aux distorsions de la représentation corporelle. Un premier axe se focalise sur l'influence interpersonnelle en testant l'effet de labels de poids sur des jugements perceptifs. Pour étudier leur influence informationnelle, nous avons fait varier la fiabilité de plusieurs signaux pour étudier la manière dont elles étaient combinées. Nos résultats indiquent que les labels de poids ont une influence réduite sur les jugements de corpulence. Un second axe porte sur l'influence médiatique. La surexposition visuelle à certains types de corps est associée à l’insatisfaction corporelle, et pourrait contribuer à expliquer certaines distorsions perceptives et attitudinales de la représentation corporelle. Dans ce contexte, l’adaptation visuelle à des corps pourrait expliquer comment l’exposition prolongée à des corps minces peut mener à une surestimation de la corpulence propre. Nous avons testé certaines hypothèses de cette théorie adaptative des distorsions de l'image du corps. Ces expériences soulignent certaines limites de la théorie adaptative ; en particulier, il est incertain que les effets d’adaptation puissent influencer la représentation que les individus ont de leurs propres corps. En conclusion, nos résultats indiquent que la dimension perceptive de la représentation de la corpulence pourrait résister à certains types d’influences sociales interpersonnelles et médiatiques<br>Body 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
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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 neurodevelopment. Although still in its infancy, medical image analysis techniques are progressively landing on the study of fetal brains. The purpose of this thesis is to develop automatic segmentation approaches that can be applied to brains at different life stages, including the gestational period, and investigate in utero brain development under ventriculomegaly.<br>En 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.
Books on the topic "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 policies, as well as examples of outright piracy. By tracing technological developments, corporate relationships, and the effects of changing musical tastes on the early record industry, Sutton provides an invaluable reference tool for all libraries and researchers concerned with recordings and the record industry.
Book chapters on the topic "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.
Full textConference papers on the topic "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-$k$ precision and nDCG@$k$). With the observation above, we develop a low-complexity large-scale multi-label learning algorithm with the goal of facilitating fast prediction and compact models by trimming tail labels adaptively. Experiments clearly verify that both the prediction time and the model size are significantly reduced without sacrificing much predictive performance for state-of-the-art approaches.
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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) framework. Theoretically, we rigorously prove the conditions for ERM learnability of MDPL in both independent and dependent cases. Furthermore, we present two MDPL algorithms under our proposed ERM framework. Comprehensive experiments on both synthetic and real-world datasets validate the effectiveness of our proposals.
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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.
Full textReports on the topic "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 model variance is below a pre-determined level. To efficiently use our framework on large computer clusters, we implemented this in Galaxy Simulation Builder, an analysis tool that is designed to work on large parallel computing environments. The program is modular, and is designed to be agnostic to the number and names of dependent variables and to the number and identifying labels of the fidelity levels. We demonstrate our multi-fidelity modeling framework on a rotorcraft collective sweep (hover) simulation and compare the accuracy and time savings of the GPR model to that of a simulation run with CFD only.