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 9 of 9 for “"Multilabel Classification"”.
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Object Detection on Unmanned Arial Vehicles Dataset Using Adaptive HydraNet
… This limitation arises from the architecture's multilabel classification step, which lacks precision in detecting small objects and consumes large amounts of computational resources. This study proposes a novel solution to overcome this limitation by introducing AHydraNet, a multitask learning …
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Algorithmic advances in learning from large dimensional matrices and scientific data
… for coding theory, that of solving large scale multilabel classification problems. We propose a new algorithm for multilabel classification which is based on group testing and codes. The algorithm has a simple inexpensive prediction method, and the error correction capabilities of codes are …
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Generalised, multilingual, optical Braille recognition models
… multiclass (well adopted methodology) and novel multilabel (proposed in this work) models on different scenarios with resampled training data. These models are evaluated on unseen test data, both in-distribution and out-of-distribution, as well as on simulated adverse conditions. The results show …
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Learning with structured decision constraints
… demonstrate how certain logical constraints in multilabel classification, such as implication, transitivity and mutual exclusivity, can be embedded in convex cones under a class of linear structured prediction models. The approach is also applicable to general affine constraints in vector …
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Multi-target Prediction Methods for Bioinformatics: Approaches for Protein Function Prediction and Candidate Discovery for Gene Regulatory Network Expansion
… prediction and more in general of hierarchical-multilabel classification (HMC). We present Ocelot a predictive pipeline for genome-wide protein characterization. It relies on a statistical-relational-learning tool, where the knowledge on the input examples is coded by the combination of multiple …
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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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Development of surface-enhanced Raman spectroscopy coupled with nanosubstrates and machine learning to improve food safety
… detection in spinach samples and achieved 98.4% classification accuracy with a mean absolute error (MAE) of 0.966 in quantification. Building upon this foundation, SERSFormer-2.0 addressed the complex challenge of detecting multiple co-existing pesticide residues in real-world produce such as …
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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. …