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 “"missing labels"”.
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Inference from Limited Observations in Statistical, Dynamical, and Functional Problems
… when the data we observe is limited: due to missing labels, small sample sizes, unobserved variables, and noise corruption. This thesis explores several problems in physics and the life sciences, where the interplay of domain knowledge with statistical theory and machine learning allows us to …
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Learning with Single View Co-training and Marginalized Dropout
… the output of partially labeled data to recover missing labels for multi-label tasks. These three algorithms not only achieve the state-of-art performance in various tasks, but also deliver orders of magnitude speed up at training and testing comparing to competing algorithms.</p>
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Overcoming uncertainty for within-network relational machine learning
… and network structure to predict corresponding labels for items; for example, to predict individuals engaged in securities fraud, we can utilize phone calls and workplace information to make joint predictions over the individuals. However, in large scale and partially observed network domains, …
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Learning from multiple heterogeneous sources - Handling source trustworthiness and incompleteness
… multiple sources can improve the quality of the labels and extend the size of the training data. However, these information sources have their own properties, and cannot be directly combined and utilized. We study the source heterogeneity from two major aspects, i.e. (1) heterogeneous quality (2) …
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Example weighting for deep representation learning
In gradient-based optimisation, the derivative of the loss of an example can be interpreted as the example’s effect on the update of a model. Consequently, a derivative magnitude function can be considered to provide a weighting scheme from the viewpoint of example weighting. Therefore, example …