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 7 of 7 for “"Sparse Bayesian Learning"”.
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Source Separation using Sparse Bayesian Learning
… linear systems, we propose utilizing Sparse Bayesian Learning and present our results on selected problems.
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Efficient Sparse Bayesian Learning using Spike-and-Slab Priors
In the context of statistical machine learning, sparse learning is a procedure that seeks a reconciliation between two competing aspects of a statistical model: good predictive power and interpretability. In a Bayesian setting, sparse learning methods invoke sparsity inducing priors to explicitly …
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Novel methods for biological network inference: an application to circadian Ca2+ signaling network
… like cells, RNA, proteins and metabolites. Learning these interactions is essential to interfering artificially with biological processes in order to, for example, improve crop yield, develop new therapies, and predict new cell or organism behaviors to genetic or environmental perturbations. …
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Direction of arrival estimation for spinning antenna based electronic intelligence systems
… biased DOA estimators were constructed using Bayesian estimation techniques and by performing a linear transformation and an affine transformation on the maximum likelihood (ML) estimator. Using Monte Carlo simulation and real radar data, we demonstrate that: the proposed biased DOA estimators …
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Process Monitoring and Control of Advanced Manufacturing based on Physics-Assisted Machine Learning
… effective quality assurance through a machine learning approach. Hence, exploring the connections between sensor data and process quality using machine learning methodologies would be advantageous. Although this direction is promising, some constraints and complex process dynamics in the actual …
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High-Dimensional Generative Models for 3D Perception
… of high-dimensional frameworks for data learning. Here, we design several sparse learning-based approaches for high-dimensional data that effectively tackle multiple perception problems, including data filtering, data recovery, and data retrieval. The frameworks offer generative solutions …
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Bayesian-based Finite Element Model Updating, Damage Detection, and Uncertainty Quantification for Cable-stayed Bridges
… sustainable maintenance. Application of Bayesian inference in SHM techniques provides a reliable platform to deal with different sources of uncertainty in the process and also to obtain probabilistic results which are more meaningful for decision-making. This research seeks to address …