Global ETD Search
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Showing 1 to 6 of 6 for “"Multi-label learning."”.
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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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Learning on the Graph: Link Prediction, Multi-label Learning, and Applications to Integrative Complex Disease Studies
… disease, are consequences of the abnormality of multiple cellular components and the perturbation of their intricate interactions. The emerging network-based approaches offer a unique framework for understanding the underlying molecular mechanism of human diseases thanks to their ability to …
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Advances in Sparse and Low Rank Matrix Optimization for Machine Learning Applications
… problems in operations research, machine learning, and statistics exhibit natural formulations as cardinality or rank constrained optimization problems. Sparse solutions are desirable for their interpretability and storage benefits. Moreover, in the machine learning setting, sparse …
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Robust Deep Learning Methodologies for Weakly Supervised Remote Sensing Image Classification
… studies and environmental monitoring. Deep learning (DL) has proven very effective in addressing the analytical challenges posed by this data, excelling in image analysis and sequential data processing. However, in remote sensing (RS), DL is often hindered by scarce and imperfect labeled …
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Heterogeneous machine learning: characterization, generation and comprehension
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-20 without embargo terms
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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 …