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 5 of 5 for “"mislabeled data"”.
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Analysis of Chemical Elements in Basalts using Mislabeled Data, a Machine Learning Approach
… target="_blank">Erbium</a> (Er). The data used for the exploration contained many outliers. Therefore, an ensemble model was created to explore the location and composition of such outliers. The ensemble was tested with synthetic data to measure performance. The synthetic data with the …
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A Bayesian classification framework with label corrections
… AT AUTHOR'S REQUEST.] The use of unlabeled data is very important for regression and classification analysis in many cases. However, the data may have an extra layer of complexity with some wrongly labelled data points. The traditional semisupervised analysis doesn’t have the mechanism to …
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Clustering Via Supervised Support Vector Machines
… clustering algorithm is introduced that clusters data with no a priori knowledge of input classes. The algorithm initializes by first running a binary SVM classifier against a data set with each vector in the set randomly labeled. Once this initialization step is complete, the SVM confidence …
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Noisy with a Chance of Mislabels: A Local and Training Dynamics Perspective on Detecting Label Noise in Deep Classification
… Detecting and mitigating the influence of mislabeled data is essential to improving both performance and interpretability. Building on insights from training dynamics, we propose Local Consistency across Training Epochs (LoCaTE), a class of data-filtering methods that leverages …
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TOWARDS RELIABLE AI UNDER DISTRIBUTION SHIFTS: A DATA-CENTRIC PERSPECTIVE
… rely on spurious correlations in the training data, leading to performance degradation and unreliability when processing inputs under distribution shifts. This thesis systematically studies the robustness to distribution shifts for ML models from a data-centric perspective. First, we closely …