Tesis sobre el tema "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.
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.
Pai, Vibha. "Evaluation of Changes between the Material and Resource Category of LEED v4.0 and v3.0 as it Pertains to New Construction and Major Renovations". University of Cincinnati / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=ucin151203942639125.
Texto completoWei, Zhihua. "The research on chinese text multi-label classification". Thesis, Lyon 2, 2010. http://www.theses.fr/2010LYO20025/document.
Texto completoLa thèse est centrée sur la Classification de texte, domaine en pleine expansion, avec de nombreuses applications actuelles et potentielles. Les apports principaux de la thèse portent sur deux points : Les spécificités du codage et du traitement automatique de la langue chinoise : mots pouvant être composés de un, deux ou trois caractères ; absence de séparation typographique entre les mots ; grand nombre d’ordres possibles entre les mots d’une phrase ; tout ceci aboutissant à des problèmes difficiles d’ambiguïté. La solution du codage en «n-grams »(suite de n=1, ou 2 ou 3 caractères) est particulièrement adaptée à la langue chinoise, car elle est rapide et ne nécessite pas les étapes préalables de reconnaissance des mots à l’aide d’un dictionnaire, ni leur séparation. La classification multi-labels, c'est-à-dire quand chaque individus peut être affecté à une ou plusieurs classes. Dans le cas des textes, on cherche des classes qui correspondent à des thèmes (topics) ; un même texte pouvant être rattaché à un ou plusieurs thème. Cette approche multilabel est plus générale : un même patient peut être atteint de plusieurs pathologies ; une même entreprise peut être active dans plusieurs secteurs industriels ou de services. La thèse analyse ces problèmes et tente de leur apporter des solutions, d’abord pour les classifieurs unilabels, puis multi-labels. Parmi les difficultés, la définition des variables caractérisant les textes, leur grand nombre, le traitement des tableaux creux (beaucoup de zéros dans la matrice croisant les textes et les descripteurs), et les performances relativement mauvaises des classifieurs multi-classes habituels
文本分类是信息科学中一个重要而且富有实际应用价值的研究领域。随着文本分类处理内容日趋复杂化和多元化,分类目标也逐渐多样化,研究有效的、切合实际应用需求的文本分类技术成为一个很有挑战性的任务,对多标签分类的研究应运而生。本文在对大量的单标签和多标签文本分类算法进行分析和研究的基础上,针对文本表示中特征高维问题、数据稀疏问题和多标签分类中分类复杂度高而精度低的问题,从不同的角度尝试运用粗糙集理论加以解决,提出了相应的算法,主要包括:针对n-gram作为中文文本特征时带来的维数灾难问题,提出了两步特征选择的方法,即去除类内稀有特征和类间特征选择相结合的方法,并就n-gram作为特征时的n值选取、特征权重的选择和特征相关性等问题在大规模中文语料库上进行了大量的实验,得出一些有用的结论。针对文本分类中运用高维特征表示文本带来的分类效率低,开销大等问题,提出了基于LDA模型的多标签文本分类算法,利用LDA模型提取的主题作为文本特征,构建高效的分类器。在PT3多标签分类转换方法下,该分类算法在中英文数据集上都表现出很好的效果,与目前公认最好的多标签分类方法效果相当。针对LDA模型现有平滑策略的随意性和武断性的缺点,提出了基于容差粗糙集的LDA语言模型平滑策略。该平滑策略首先在全局词表上构造词的容差类,再根据容差类中词的频率为每类文档的未登录词赋予平滑值。在中英文、平衡和不平衡语料库上的大量实验都表明该平滑方法显著提高了LDA模型的分类性能,在不平衡语料库上的提高尤其明显。针对多标签分类中分类复杂度高而精度低的问题,提出了一种基于可变精度粗糙集的复合多标签文本分类框架,该框架通过可变精度粗糙集方法划分文本特征空间,进而将多标签分类问题分解为若干个两类单标签分类问题和若干个标签数减少了的多标签分类问题。即,当一篇未知文本被划分到某一类文本的下近似区域时,可以直接用简单的单标签文本分类器判断其类别;当未知文本被划分在边界域时,则采用相应区域的多标签分类器进行分类。实验表明,这种分类框架下,分类的精确度和算法效率都有较大的提高。本文还设计和实现了一个基于多标签分类的网页搜索结果可视化系统(MLWC),该系统能够直接调用搜索引擎返回的搜索结果,并采用改进的Naïve Bayes多标签分类算法实现实时的搜索结果分类,使用户可以快速地定位搜索结果中感兴趣的文本。
Mayouf, Mouna Sabrine. "Intégration de connaissances de haut-niveau dans un système d'apprentissage par réseau de neurones pour la classification d'images". Electronic Thesis or Diss., Toulouse 3, 2023. http://www.theses.fr/2023TOU30341.
Texto completoNeural networks have made remarkable improvements in challenging tasks such as automatic image classification and natural language processing. However, their black-box nature hinders explainability and limits their ability to leverage external knowledge. The purpose of this thesis is to explore and propose techniques for integrating knowledge into neural networks in order to improve their performance and interpretability. The first part of the thesis focuses on integrating knowledge at the input level. The first chapter deals with data preparation. A formalization of pre-processing is proposed to ensure the transparency and reproducibility of this step. This formalization enables us to study the impact of data augmentation: to characterize a good data preparation, and the informative state of a dataset, a set of measures and principles is proposed, then experimental protocols are designed to evaluate these principles on the BreakHis dataset. The second chapter of this part focuses on exploiting high-level knowledge to determine the order in which data should be inserted into the network. We introduce an incremental curriculum learning for ordering the input data. The results obtained show an improvement of accuracy and convergence speed. Although this study is carried out on the BreakHis dataset, we believe that it can be generalized to any other dataset. The second part is devoted to the integration of knowledge within the network architecture and at the output level. In this context, we focus on hierarchical multi-label classification, for which we formalize the knowledge representing the hierarchical link. For this aim, we introduce two constraints: one representing the fact that an object can only be assigned to one class at a given level of the hierarchy, and the other imposing that the global assignment of an object respects the class hierarchy (for example, we forbid classifying an element as a bee for its sub-type and a mammal for its super-type). We design an architecture and a loss function that impose these two constraints during learning. The architecture differs from the state of the art in that a single network is used to simultaneously predict the labels of the different levels: all layers are responsible for predicting the tuple of classes. Several variants of the network have been tested on five different datasets and the results confirm the efficiency of the hierarchical constraints, thus supporting the importance of taking external knowledge into account. In order to refine the results of this hierarchical classification, we introduce an abstention mechanism, in the form of a third constraint that enforces the network to give a prediction at the most precise level of specificity on which its confidence is sufficient and to abstain otherwise. We define different confidence thresholds and proposed different constraints on the thresholds accordingly to the class hierarchy. To evaluate this mechanism, new classification metrics that take abstention into account are defined. We carry out experiments on the same five datasets and the results show the interest of abstention, and the need to define empirical thresholds adapted to each dataset. In conclusion, the work in this thesis highlights the value of exploiting external knowledge, this is true for the three main components of a neural network: at the input level during data preparation, in the structure of the network, and at the output level when classification decisions are made
Valera, Silvia. "PELDOR in multi-spin systems : from model systems synthesis to biological applications". Thesis, University of St Andrews, 2016. http://hdl.handle.net/10023/16960.
Texto completoHe, Yuanye y 何原野. "Identifying Labels from Multi-label Texts Using Deep Learning". Thesis, 2017. http://ndltd.ncl.edu.tw/handle/xh4hf3.
Texto completo元智大學
資訊工程學系
105
With the development of society, more and more attention has been paid to psychiatric health and psychiatric illness. When people are depressed or suffer from psychiatric illness, it is an efficient and effective way to seek help from the Internet to help them to alleviate their suffering. Many psychiatric health websites have established forums and blogs to help people share their psychological problems with other users and psychologists. Other users and psychologists can give advice on how to respond to these psychological problems. Psychiatric health Web site has accumulated a large number of descriptions of psychological illness, which contains a wealth of emotion labels to express different psychological illness. Automatic identification of these mental illness labels can make online mental health services more efficient. In this paper, we propose a combined depth neural network framework BLSTM_CNN model to extract features from the text automatically. BLSTM is used to extract the sentence for each word, CNN is used to extract local features in a word, through the combination of BLSTM and CNN, according to the different emotion labels, implied more useful features can be extracted. The experimental results show that the BLSTM_CNN model is better than the CNN, LSTM and LSTM_CNN models.
Chung, Chih-Heng y 鍾至衡. "Improving Semi-supervised Multi-label Classification by Training Labels Recovery with Consensus Clustering". Thesis, 2019. http://ndltd.ncl.edu.tw/handle/u7ekqf.
Texto completo國立臺灣科技大學
資訊工程系
107
The problem of semi-supervised classification with non-uniformly distributed incomplete labels is frequently encountered in real world applications. The lack of positive information, the absence of negative examples and the non-uniform distribution of missing labels lead to the diminished accuracy of multi-label classification results. In this research, we propose the Semi-supervised Incomplete Training Label Recovery (SITLR) algorithm to solve the semi-supervised multi-label classification with incompletely labeled training data. With the proposed weight adjustment step and negative information initialization with LF-CARS algorithm, SITLR focuses on enhancing the information of labeled training instances according to the distribution of data, where it only recovers some important labels and the recovered training data can be applied to any existing multi-label classification algorithm for building a better classification model and generating better label predictions in the testing phase. The experiments verified the effectiveness of SITLR.
(9187466), Bharath Kumar Comandur Jagannathan Raghunathan. "Semantic Labeling of Large Geographic Areas Using Multi-Date and Multi-View Satellite Images and Noisy OpenStreetMap Labels". Thesis, 2020.
Buscar texto completoKai-ShengChen y 陳楷升. "Investigation of Spectral-Amplitude Coding Labels for Packet-Switching Applications over Generalized Multi-Protocol Label Switching Network". Thesis, 2017. http://ndltd.ncl.edu.tw/handle/fa9e72.
Texto completo國立成功大學
電腦與通信工程研究所
105
Internet protocol (IP) is the most widely used protocol for high-bandwidth data transmission and it has been thought as a solution to provide different high-quality services in the future. As the internet traffic increases rapidly, the network size is extended. Multi-protocol label switching (MPLS) is proposed to reduce the IP processing time because only label is processed during the packet transmission between nodes. Although MPLS partially releases the burden of IP network, packet routing still faces a bottleneck when the number of users is large. Optical packet switching (OPS) overcomes this difficulty by simplifying several layers into IP over optical network. To implement MPLS over optical work, optical codes (OC) are used as labels for packet switching in Generalized MPLS (GMPLS) network. Among several label approaches, spectral amplitude coding (SAC) lowers system complexity and is compatible with label stacking. The label of an optical pocket is composed by different wavelength components, which are encoded according to a signature code pattern. However, due to the incoherent property of light source, the phase intensity induced noise (PIIN) appears at the forwarding node when the optical code label is de-coded. PIIN cannot simply removed by increasing the signal power because its value is proportional to the detected optical current. Therefore, we design three optical code labelling (OCL) scenarios, to increase the probability of correctly decoding the label in core nodes (CNs). Since the packet is sent to the appropriate path, the label error rate (LER) at edge node (EN) is decreased. In the first approach, stuffed quadratic congruence code (SQC code) is proposed for optical label implementing. Because of its low cross-correlation value, the effect of PIIN can be decreased significantly. If the label can be decoded correctly, the forward node would generate proper control signal to direct the packet to a suitable path. This reduces the probability of packet missing and lowers the value of LER when optical packet is de-modulated at the end node. For the case of label stacking, labels with SQC codes can provide greater system improvements. To meet practical applications, the relation between SAC-labels and optical MPLS network performance is also analyzed in this dissertation by numerical simulation. In the second approach, a hybrid label for optical packet switching in GMPLS network is proposed by combining SAC optical code-division multiple access (OCDMA) with wavelength division multiplexing (WDM). The author considers two label assignment scenarios. Hybrid labels are sequentially assigned to path segments in a label switching path (LSP) based on code index or wavelength index. LER performance of these two label assignment scenarios are also analyzed. Better LER results is achieved by sequential wavelength assignment, due to the similar label numbers among wavelengths. Furthermore, the optimal channel number is derived to minimize the LER under a specific number of stacked labels. In the final approach, bipolar OCL is employed in GMPLS network to improve the efficiency of label-recognition and network throughput. Label switching capabilities in LER is greatly reduced since the proposed bipolar OCL enlarges the Hamming distance of the star diagram of the decoded label signals. The proposed label mapping mechanism is also achieved through SAC in physical layer. In performance analysis, a numerical simulation of LER is presented to quantify the switching efficiency. Results show the proposed bipolar coding technique reduces LER in switching process, resulting in an extension of LSP in GMPLS core network.
Mandal, Devraj. "Cross-Modal Retrieval and Hashing". Thesis, 2020. https://etd.iisc.ac.in/handle/2005/4685.
Texto completo