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Full-Text Articles in Physical Sciences and Mathematics
Towards Deeper Understanding In Neuroimaging, Rex Devon Hjelm
Towards Deeper Understanding In Neuroimaging, Rex Devon Hjelm
Computer Science ETDs
Neuroimaging is a growing domain of research, with advances in machine learning having tremendous potential to expand understanding in neuroscience and improve public health. Deep neural networks have recently and rapidly achieved historic success in numerous domains, and as a consequence have completely redefined the landscape of automated learners, giving promise of significant advances in numerous domains of research. Despite recent advances and advantages over traditional machine learning methods, deep neural networks have yet to have permeated significantly into neuroscience studies, particularly as a tool for discovery. This dissertation presents well-established and novel tools for unsupervised learning which aid in …
Biogeographical Patterns Of Soil Microbial Communities: Ecological, Structural, And Functional Diversity And Their Application To Soil Provenance, Natalie Damaso
FIU Electronic Theses and Dissertations
The current ecological hypothesis states that the soil type (e.g., chemical and physical properties) determines which microbes occupy a particular soil and provides the foundation for soil provenance studies. As human profiles are used to determine a match between evidence from a crime scene and a suspect, a soil microbial profile can be used to determine a match between soil found on the suspect’s shoes or clothing to the soil at a crime scene. However, for a robust tool to be applied in forensic application, an understanding of the uncertainty associated with any comparisons and the parameters that can significantly …
Online Cross-Validation-Based Ensemble Learning, David Benkeser, Samuel D. Lendle, Cheng Ju, Mark J. Van Der Laan
Online Cross-Validation-Based Ensemble Learning, David Benkeser, Samuel D. Lendle, Cheng Ju, Mark J. Van Der Laan
U.C. Berkeley Division of Biostatistics Working Paper Series
Online estimators update a current estimate with a new incoming batch of data without having to revisit past data thereby providing streaming estimates that are scalable to big data. We develop flexible, ensemble-based online estimators of an infinite-dimensional target parameter, such as a regression function, in the setting where data are generated sequentially by a common conditional data distribution given summary measures of the past. This setting encompasses a wide range of time-series models and as special case, models for independent and identically distributed data. Our estimator considers a large library of candidate online estimators and uses online cross-validation to …
Multiple Imputation Of Missing Data In Structural Equation Models With Mediators And Moderators Using Gradient Boosted Machine Learning, Robert J. Milletich Ii
Multiple Imputation Of Missing Data In Structural Equation Models With Mediators And Moderators Using Gradient Boosted Machine Learning, Robert J. Milletich Ii
Psychology Theses & Dissertations
Mediation and moderated mediation models are two commonly used models for indirect effects analysis. In practice, missing data is a pervasive problem in structural equation modeling with psychological data. Multiple imputation (MI) is one method used to estimate model parameters in the presence of missing data, while accounting for uncertainty due to the missing data. Unfortunately, commonly used MI methods are not equipped to handle categorical variables or nonlinear variables such as interactions. In this study, we introduce a general MI framework that uses the Bayesian bootstrap (BB) method to generate posterior inferences for indirect effects and gradient boosted machine …
Learning From Data: Plant Breeding Applications Of Machine Learning, Alencar Xavier
Learning From Data: Plant Breeding Applications Of Machine Learning, Alencar Xavier
Open Access Dissertations
Increasingly, new sources of data are being incorporated into plant breeding pipelines. Enormous amounts of data from field phenomics and genotyping technologies places data mining and analysis into a completely different level that is challenging from practical and theoretical standpoints. Intelligent decision-making relies on our capability of extracting from data useful information that may help us to achieve our goals more efficiently. Many plant breeders, agronomists and geneticists perform analyses without knowing relevant underlying assumptions, strengths or pitfalls of the employed methods. The study endeavors to assess statistical learning properties and plant breeding applications of supervised and unsupervised machine learning …
Data Driven Sample Generator Model With Application To Classification, Alvaro Emilio Ulloa Cerna
Data Driven Sample Generator Model With Application To Classification, Alvaro Emilio Ulloa Cerna
Mathematics & Statistics ETDs
Despite the rapidly growing interest, progress in the study of relations between physiological abnormalities and mental disorders is hampered by complexity of the human brain and high costs of data collection. The complexity can be captured by machine learning approaches, but they still may require significant amounts of data. In this thesis, we seek to mitigate the latter challenge by developing a data driven sample generator model for the generation of synthetic realistic training data. Our method greatly improves generalization in classification of schizophrenia patients and healthy controls from their structural magnetic resonance images. A feed forward neural network trained …
Privacy And Accountability In Black-Box Medicine, Roger Allan Ford, W. Nicholson Price Ii
Privacy And Accountability In Black-Box Medicine, Roger Allan Ford, W. Nicholson Price Ii
Law Faculty Scholarship
Black-box medicine—the use of big data and sophisticated machine learning techniques for health-care applications—could be the future of personalized medicine. Black-box medicine promises to make it easier to diagnose rare diseases and conditions, identify the most promising treatments, and allocate scarce resources among different patients. But to succeed, it must overcome two separate, but related, problems: patient privacy and algorithmic accountability. Privacy is a problem because researchers need access to huge amounts of patient health information to generate useful medical predictions. And accountability is a problem because black-box algorithms must be verified by outsiders to ensure they are accurate and …