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 20 of 88 for “"sparse data"”.
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Investigating Comparison-based Evaluation for Sparse Data
… the purpose. However, in practice evaluation data are typically extremely sparse and each entity would receive a very small number of ratings from evaluators. In this case, the average ratings would significantly differ from the true ratings due to biased distributions of evaluators holding …
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Learning Spatio-Temporal Correlations from Dynamic and Sparse Data
… measurement semantics over time, resulting in datasets with dynamic spatial distributions. Existing spatial learning approaches often fall short in capturing spatio-temporal patterns from these distributions. One of the affected environmental fields is physical oceanography, which is the second …
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Provably Asymptotically Near-Optimal Motion Planning with Sparse Data Structures
… near-optimal solutions produce sparser graphs by notincluding all edges. The idea stems from graph spanner algorithms,which produce sparse subgraphs that guarantee near-optimal paths.Existing asymptotically optimal and near-optimal planners, however,include all sampled …
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Robotic radiation mapping using modelling and probabilistic analysis of sparse data
… the goal of generating radiation maps using sparse data.
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Machine Learning Based Analysis Of Civil Infrastructure In The Presence Of Sparse Data
<p>The high computational cost of estimating engineering demand parameters (EDPs) via finite element (FE) models, which incorporate uncertainties in earthquake events and material properties, limits the application of the Performance-Based Earthquake Engineering (PBEE) framework. Previous efforts …
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A subspace method for reconstruction of time-series fMRI images from sparse data
… use outside of research settings due to its long data acquisition time and the large amount of data required to generate useful results. Methods which reduce the amount of required data while maintaining acceptable results become necessary to allow the availability of fMRI in clinical settings. …
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Fine-grained memory access over I/O interconnect for efficient remote sparse data access
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2022-11-11 without embargo terms
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Stress tensor estimates derived from focal mechanism solutions of sparse data sets: applications to seismic zones in Virginia and eastern Tennessee
… The MSET is useful when applied to small data sets, and differs from existing techniques in that (1) the use of multiple focal mechanisms for individual earthquakes allows for a range in the possible orientation of the fault geometry, while preserving fit to the original polarity and …
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Exploration of Techniques for Working with Sparse Data when Applying Natural Language Processing to Assist a Qualitative Data Analysis of a COVID-19 Open Innovation Community
… Language Processing (NLP) with Qualitative Data Analysis (QDA) to investigate the dynamics of volunteer involvement within the TeamOSV community, a collective formed in response to the COVID-19 pandemic. Central to this study is the exploration of roles and interaction patterns among …
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Classification with Large Sparse Datasets: Convergence Analysis and Scalable Algorithms
Large and sparse datasets, such as user ratings over a large collection of items, are common in the big data era. Many applications need to classify the users or items based on the high-dimensional and sparse data vectors, e.g., to predict the profitability of a product or the age group of a user, …
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Monte-Carlo-simulation-based performance analysis of the heat transfer process in nuclear-based hydrogen production based on Cu-Cl Cycle.
… analysis to the heat transfer process is the sparse data. In this thesis, the methodology of Monte-Carlo Simulation-based (MCS-based) performance analysis is developed, and it is shown that this method can be used to deal effectively with the problems caused by sparse data in the heat transfer …
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Statistical Hierarchical Modelling for Industrial Collaborative Prognosis
… telecommunications, and metrology have propelled data-driven decision making across the industries. Industrial health management in particular has been increasingly reliant on Machine Learning techniques for data-driven prognosis as modern assets are exhaustively monitored by their embedded …
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An improved self organizing map using jaccard new measure for textual bugs data clustering
In software projects there is a data repository which contains the bug reports. These bugs are required to carefully analyze to resolve the problem. Handling these bugs humanly is extremely time consuming process, and it can result the delaying in addressing some important bugs resolutions. To …
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A retrospective study of CT angiography versus digital subtraction angiography in penetrating neck trauma
… neck trauma remains controversial. There is only sparse data validating the use of Computed Tomography Angiography (CTA) in the evaluation of penetrating neck trauma. OBJECTIVES. To assess the sensitivity and specificity of CTA versus Digital Subtraction Angiography (DSA) in detecting arterial …
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Beyond Parameter Estimation: Analysis of the Case-Cohort Design in Cox Models
… procedures. Cases are over-represented in the dataset, and hence estimation of coefficients in this design requires weighting of observations. This results in a pseudopartial likelihood, and standard post-estimation methods may not be readily transferable to the case-cohort design. This thesis …
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Statistical Modeling to Investigate Anatomy and Function of the Knee
… in shape and motion and make predictions from sparse data.</p> <p>Statistical models were used to investigate relationships between natural knee anatomy and kinematics and make predictions of both shape and function from sparse data. A whole-joint characterization study identified key …
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Prediction Intervals: The Effects and Identification of Sparse Regions for Nonparametric Regression Methods
… how well they perform when making predictions in sparse regions of the predictor space. This sparsity is an extension to the more common concept of extrapolation in linear regression settings. Using simulation studies, we show that coverage probabilities using prediction intervals from quantile …
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Social inductive biases for reinforcement learning
… sample efficient when learning from each other's sparse data, and under time constraints? At the root of these questions is a simple one: how do agents, human or machines, learn from each other, and can we improve it and apply it to new domains? The cognitive and social sciences have provided …
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