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 4 of 4 for “"Deep Embedding"”.
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Deep Embedding Kernel
Kernel methods and deep learning are two major branches of machine learning that have achieved numerous successes in both analytics and artificial intelligence. While having their own unique characteristics, both branches work through mapping data to a feature space that is supposedly more …
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Deep embedding approach to classify purpose of trips between cities from GPS data
… of trips between cities from GPS traces using a deep embedding approach. I extracted statistical features that captures trips characteristics that includes: temporal features, spatial features and Points of Interests (POI) features. I deployed a deep learning model to extract representative …
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Multi-scale Deep Nearest Neighbors
In this thesis, we aim to learn a deep embedding space suitable for k-NN. Our approach is based on minimizing the leave-one-out 1-NN classification error in the embedding space. Directly optimizing for such a rule is not tractable due to its discontinuous nature. We propose Multi-scale Deep Nearest …
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Similarity learning in the era of big data
This 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 …