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 550 for “"supervised learning"”.
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Federated self-supervised learning
Federated learning (FL) has garnered significant attention from both research and industrial communities due to its distinctive ability to facilitate collaborative learning from large-scale datasets without compromising users’ data privacy. However, current FL practices predominantly focus on …
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Self-Supervised Learning for Geometry
… we cast the geometric problems as machine learning problems, specifically, deep learning problems. Differ from conventional supervised learning methods that using expensive annotations as the supervisory signal, we advocate for the use of geometry as a supervisory signal to improve the …
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Coupled similarity analysis in supervised learning
In supervised learning, the distance 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 …
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Semi-supervised learning for natural language
Statistical supervised learning techniques have been successful for many natural language processing tasks, but they require labeled datasets, which can be expensive to obtain. On the other hand, unlabeled data (raw text) is often available "for free" in large quantities. Unlabeled data has shown …
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Self-Supervised Learning for Speech Processing
Deep neural networks trained with supervised learning algorithms on large amounts of labeled speech data have achieved remarkable performance on various spoken language processing applications, often being the state of the arts on the corresponding leaderboards. However, the fact that training …
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Visual Domain Generalization via Self-Supervised Learning
L'abstract è presente nell'allegato / the abstract is in the attachment
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Self-supervised learning frameworks for IoT applications
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2022-11-15 without embargo terms
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Multiple-implementation testing of supervised learning software
Machine Learning (ML) software, used to implement an ML algorithm, is widely used in many application domains such as financial, business, and engineering domains. Faults in ML software can cause substantial losses in these application domains. Thus, it is very critical to conduct effective testing …
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Structuring Representation Geometry in Self-Supervised Learning
The central promise of deep learning is to learn a map 𝑓 : 𝒳 → ℝ_𝑑 that transforms objects 𝒳—represented in their raw perceptual forms, such as images or molecular strings—into a representation space ℝ_𝑑 where everything that is hard to do with raw perceptual data becomes easy. For instance, …
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Big Data Algorithms for Visualization and Supervised Learning
… extract useful knowledge, researchers in machine learning and data mining communities are faced with numerous challenges, since the data mining and machine learning tools designed for standard desktop computers are not capable of addressing these problems due to memory and time constraints. As a …
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Ensemble-based Supervised Learning for Predicting Diabetes Onset
… This thesis presents a tool based on a machine learning ensemble for predicting diabetes onset. Ensembles often perform better than a single classifier, and accuracy and diversity have been highlighted as the two vital requirements for constructing good ensemble classifiers. Experiments in this …
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Self-supervised Learning Methods for Vision-based Tasks
… to leverage this data for training many machine learning models. Among them, self-supervised learning appears as an efficient solution capable of training powerful and generalizable models. More specifically, instead of relying on human-generated labels, it proposes training objectives that use …
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Self-supervised Learning of Monocular Depth from Video
… problem of monocular depth estimation via self-supervised learning from RGB-only videos. Although existing work has shown partial excellent results in benchmark datasets, there remain several vital challenges that limit the use of these algorithms in general scenarios. To summarize, my …
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Active and Semi-Supervised Learning for Speech Recognition
… to the combination of the rise in deep learning in speech recognition and an increase in computing power. The increase in computing power enabled the training of models on ever-expanding data sets, and deep learning allowed for the better exploitation of these large data sets. For …
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The information regularization framework for semi-supervised learning
… be missing the class label. While traditional supervised classifiers already have the ability to cope with some incomplete data, the new type of classifiers do not view unlabeled data as an anomaly, and can learn from data sets in which the large majority of training points are unlabeled. …
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Self-supervised Learning for IMU-based Human Activity Recognition
… In this thesis, we propose the use of self-supervised learning for human activity recognition using the tri-axial data collected from the smartphone-embedded accelerometers. To address the limitations of fully-supervised learning, mainly reliance on labeled data, we propose two …
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Restricting Supervised Learning: Feature Selection and Feature Space Partition
Many supervised learning problems are considered difficult to solve either because of the redundant features or because of the structural complexity of the generative function. Redundant features increase the learning noise and therefore decrease the prediction performance. Additionally, a number …
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