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 26 for “"Multi-label classification"”.
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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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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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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
… (CNNs), which have proven effective in image classification tasks. Plant leaves, often exhibiting symptoms such as discoloration and irregular textures, serve as key indicators for disease detection. By processing large datasets of leaf images, CNNs can automate disease diagnosis without the …
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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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A Knowledge Graph based Method on Language Understanding for Customer Service
… simple similarity match and hierarchical multi-label classification on hierarchical knowledge to effective answer user’s input question in human language. In addition, we explore a new model named <strong>Hierar-BERT-RCNN</strong> to recognize and classify vague question in hierarchical …
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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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Content-based Image Understanding with Applications to Affective Computing and Person Recognition in Natural Settings
… proposals and the mutual relationship between multiple labels is developed to boost multi-label classification. It is evaluated both on object recognition and aesthetic attributes learning. We also develop a person detection and recognition system in natural settings that can robustly handle …
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An automated framework for problem report triage in large-scale open source problem repositories
… (Wontfix) it is annotated with the appropriate label. If the report is deemed to describe a new problem, it is assigned to a developer to work on a solution. In instances when the report is a duplicate, it is assigned the report number associated with the original problem report.;In typical …
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Leveraging expression and network data for protein function prediction
… protein function prediction is a hierarchical multi-label classification problem. The classification method used in this thesis is GOstruct, which performs structured predictions that take into account all GO terms. GOstruct has been shown to work well, but there are still improvements to be …
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Cost-Based Optimization for Semantic Operator Systems
… on a range of workloads including biomedical multi-label classification (BioDEX), information extraction from legal contracts (CUAD), and multi-modal question answering (MMQA). We demonstrate that systems optimized by our work achieve 18.7%-39.2% better quality and up to 23.6x lower cost and …
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Adversarial machine learning in computer vision: attacks and defenses on machine learning models
… a variety of application domains such as image classification, natural language processing, and malware detection. However, deep neural networks are demonstrated to be vulnerable to adversarial examples at the test time. Adversarial examples are malicious inputs generated from the legitimate …
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Associative Pattern Recognition for Biological Regulation Data
… factors, histone modifications and functional labels using heterogeneous data sources (numeric, sequences, time series data and textual labels). </p> <p>In protein-DNA associative pattern recognition, we introduce an efficient algorithm for affinity test by searching for over-represented DNA …
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Coupled similarity analysis in supervised learning
… or similarity measure is widely used in a lot of classification algorithms. When calculating the categorical data similarity, the strategy used by the traditional classifiers often overlooks the inter-relationship between different data attributes and assumes that they are independent of each …
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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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Deep Learning for Early Detection, Identification, and Spatiotemporal Monitoring of Plant Diseases Using Multispectral Aerial Imagery
… the development of automatic and accurate image classification systems. These advances coupled with the widespread availability of multispectral aerial imagery provide a cost-effective method for developing crop-diseases classification tools. However, large datasets are required to train deep …
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Natural Language Programming for Controlled Object-Oriented English
… structural analysis with the data-based multi-label classification (MLC) method. Experiment results and user studies show that, with the proposed model and approaches reducing the ambiguity within the target domain, the NLPr system can process a relatively expressive controlled NL for …
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Parasite communities and their identification in wild and domestic herbivores in Iceland
… The models were trained using random forest multi-label classification algorithms with binary relevance (BR), classifier chain (CC) and label powerset (LP) frameworks to calibrate NIRS and MIRS spectra to nemabiome results. All models were able to identify parasite species from frozen …
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Evaluating Adversarial Robustness of Detection-based Defenses against Adversarial Examples
… This approach can be applied in both single and multi-label classification settings. The second one is based on a Deep Neural Rejection (DNR) mechanism to detect adversarial examples, based on the idea of rejecting samples that exhibit anomalous feature representations at different network …
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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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