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.
Results
Showing 1 to 5 of 5 for “"Positive-Unlabeled Learning"”.
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Towards Uncovering the True Use of Unlabeled Data in Machine Learning
Knowing how to exploit unlabeled data is a fundamental problem in machine learning. This dissertation provides contributions in different contexts, including semi-supervised learning, positive unlabeled learning and representation learning. In particular, we ask (i) whether is possible to learn a …
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Machine learning for understanding protein sequence and structure
… This thesis introduces a variety of machine learning methods for accelerating protein structure determination by cryoEM and for learning from large protein databases. We first consider the problem of protein identification in the large images collected in cryoEM. We propose a …
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Information filtering by multiple examples
… keywords. Most of the studies on SBME adopt the Positive Unlabeled learning (PU learning) techniques by treating the user's provided examples (denoted as query examples) as positive set and the entire data collection in the database as unlabeled set. User's information need is then represented as …
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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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Similarity learning in the era of big data
… dissertation studies the problem of similarity learning in the era of big data with heavy emphasis on real-world applications in social media. As in the saying “birds of a feather flock together,” in similarity learning, we aim to identify the notion of being similar in a data-driven and …