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Full-Text Articles in Physical Sciences and Mathematics
Graph-Based Latent Embedding, Annotation And Representation Learning In Neural Networks For Semi-Supervised And Unsupervised Settings, Ismail Ozsel Kilinc
Graph-Based Latent Embedding, Annotation And Representation Learning In Neural Networks For Semi-Supervised And Unsupervised Settings, Ismail Ozsel Kilinc
USF Tampa Graduate Theses and Dissertations
Machine learning has been immensely successful in supervised learning with outstanding examples in major industrial applications such as voice and image recognition. Following these developments, the most recent research has now begun to focus primarily on algorithms which can exploit very large sets of unlabeled examples to reduce the amount of manually labeled data required for existing models to perform well. In this dissertation, we propose graph-based latent embedding/annotation/representation learning techniques in neural networks tailored for semi-supervised and unsupervised learning problems. Specifically, we propose a novel regularization technique called Graph-based Activity Regularization (GAR) and a novel output layer modification called …
Energy Efficient Context-Aware Framework In Mobile Sensing, Ozgur Yurur
Energy Efficient Context-Aware Framework In Mobile Sensing, Ozgur Yurur
USF Tampa Graduate Theses and Dissertations
The ever-increasing technological advances in embedded systems engineering, together with the proliferation of small-size sensor design and deployment, have enabled mobile devices (e.g., smartphones) to recognize daily occurring human based actions, activities and interactions. Therefore, inferring a vast variety of mobile device user based activities from a very diverse context obtained by a series of sensory observations has drawn much interest in the research area of ubiquitous sensing. The existence and awareness of the context provides the capability of being conscious of physical environments or situations around mobile device users, and this allows network services to respond proactively and intelligently …