Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 61 for “"multi-label"”.
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Scalable Multi-label Classification
Multi-label classification is relevant to many domains, such as text, image and other media, and bioinformatics. Researchers have already noticed that in multi-label data, correlations exist between labels, and a variety of approaches, drawing inspiration from many spheres of machine learning, have …
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Advanced topics in multi-label learning
Multi-label learning, in which each instance can belong to multiple labels simultaneously, has significantly attracted the attention of researchers as a result of its wide range of applications, which range from document classification and automatic image annotation to video annotation. Many …
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Multi-label/multi-class Deep Learning Classification Of Spatiotemporal Data
… activity in machine learning requires the use of multi-labels. In order to detect concurrent occurrences spatially, the labels should represent the regions of interest for a particular application. For example, in this thesis, the regions of interest will be either different quadrants of a parking …
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Multi-Label/Multi-Class Deep Learning Classification of Spatiotemporal Data
… activity in machine learning requires the use of multi-labels. In order to detect concurrent occurrences spatially, the labels should represent the regions of interest for a particular application. For example, in this thesis, the regions of interest will be either different quadrants of a parking …
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Single and Multi-Label Environmental Sound Classification Using Convolutional Neural Networks
… of artificial neural net-works for single-label and multi-label multiclass classification of environmental sounds like dog bark, street music or jackhammer. Evaluation to di˙erent cor-ruptions of the sounds are studied, as well as methods to increase robustness to these variations. A …
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Hierarchical multi-label classification for protein function prediction going beyond traditional approaches
<p>Hierarchical multi-label classification is a variant of traditional classification in which the</p> <p>instances can belong to several labels, that are in turn organized in a hierarchy. Functional classification of genes is a challenging problem in functional genomics due to several reasons. …
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Meta-aprendizado para análise de desempenho de métodos de classificação multi-label
… se restringem a utilizarem algoritmos singlelabel, ou seja, que atribuem apenas uma classe a uma dada instância. Tais aplicações se tornam inadequadas quando essa mesma instância, no mundo real, pertence a mais de uma classe simultaneamente. Tal problema é denominado na literatura como …
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Deriving Classifiers with Single and Multi-Label Rules using New Associative Classification Methods
… one class per rule and ignore other class labels even when they have large data representation. Thus, extending current AC algorithms to find and extract multi-label rules is promising research direction since new hidden knowledge is revealed for decision makers. Furthermore, the …
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Enhancing multi-label object recognition in complex images via region-based continual learning
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-20 without embargo terms
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Examination of machine learning methods for multi-label classification of intellectual property documents
… of machine learning techniques for the task of multi-label document classification applied to a corpus of United States patent grants. The rapidly rising number of patent applications in the past several decades has led to a rising need for enhanced automatic patent processing tools. The task of …
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Advancing Explainability in Multi-Label Classification for Tomato Disease Detection Using Machine Learning Interpretability Techniques
… leaf diseases, where the plant may suffer from multiple diseases simultaneously. Unlike traditional multiclass classification, which labels diseases as individual categories, multilabel classification allows for the identification of iv multiple diseases in a single leaf. This multilabel …
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Learning on the Graph: Link Prediction, Multi-label Learning, and Applications to Integrative Complex Disease Studies
… disease, are consequences of the abnormality of multiple cellular components and the perturbation of their intricate interactions. The emerging network-based approaches offer a unique framework for understanding the underlying molecular mechanism of human diseases thanks to their ability to …
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Coupled similarity analysis in supervised learning
… the classification tasks of class-imbalance or multi-label. In order to solve these research limitations and challenges, this thesis proposes an insightful analysis on coupled similarity in supervised learning to give an expression of similarity that is more closely related to the real nature of …
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Deep image representation learning for knowledge discovery from earth observation data archives
… modelling RS image similarities by exploiting multi-label training images; iii) time efficient and scalable information extraction; iv) effective IRL under noisy training labels; and v) joint use of multiple learning tasks for describing the complex content of RS images. This thesis aims to …
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Fine-grained sentiment analysis for customer review
… sentiment analysis due to the lack of well-labeled aspect-level dataset. This thesis propose a semi-supervised approach using pre-trained BERT model to conduct the fine-grained aspect sentiment analysis, and tests it on the benchmark dataset SemEval2014. Our proposed Sentiment Mask Enhanced …
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Evaluating convolutional neural networks and transformer architectures for image-based prediction of protein localization in eukaryotic cells
… and Transformer- based models for the multi-label classification of protein subcellular localization in eukaryotic cells, using large-scale immunofluorescence image datasets. Methods: In this study, we comparatively evaluated convolutional neural network (CNN) architectures …
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Optimization of Markov Random Fields in Computer Vision
… a memory efficient max-flow algorithm for multi-label submodular MRFs. In fact, such MRFs have been shown to be optimally solvable using max-flow based on an encoding of the labels proposed by Ishikawa, in which each variable $X_i$ is represented by $\ell$ nodes (where $\ell$ is the number …
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Optimization of Markov Random Fields in Computer Vision
… a memory efficient max-flow algorithm for multi-label submodular MRFs. In fact, such MRFs have been shown to be optimally solvable using max-flow based on an encoding of the labels proposed by Ishikawa, in which each variable $X_i$ is represented by $\ell$ nodes (where $\ell$ is the number …
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Efficient and trustworthy methods for knowledge discovery
… tasks that arise naturally in modern data: multi-label classification and community search in temporal graphs. Regarding multi-label classification, we propose an efficient and accurate rule-based multi-label classifier that drastically improves upon the interpretability of existing …
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