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Articles 1 - 5 of 5
Full-Text Articles in Physical Sciences and Mathematics
Machine Learning Morphisms: A Framework For Designing And Analyzing Machine Learning Work Ows, Applied To Separability, Error Bounds, And 30-Day Hospital Readmissions, Eric Zenon Cawi
McKelvey School of Engineering Theses & Dissertations
A machine learning workflow is the sequence of tasks necessary to implement a machine learning application, including data collection, preprocessing, feature engineering, exploratory analysis, and model training/selection. In this dissertation we propose the Machine Learning Morphism (MLM) as a mathematical framework to describe the tasks in a workflow. The MLM is a tuple consisting of: Input Space, Output Space, Learning Morphism, Parameter Prior, Empirical Risk Function. This contains the information necessary to learn the parameters of the learning morphism, which represents a workflow task. In chapter 1, we give a short review of typical tasks present in a workflow, as …
Genetics Of Pediatric Musculoskeletal Disorders, Lilian Antunes
Genetics Of Pediatric Musculoskeletal Disorders, Lilian Antunes
Arts & Sciences Electronic Theses and Dissertations
Pediatric musculoskeletal disorders are an extremely broad category of diseases that are often inherited. While individually rare, collectively these disorders are common, affecting around 3% of live births in the US. Despite the mounting clinical and molecular evidence for a genetic etiology, the cause for many patients with pediatric musculoskeletal disorders remain largely unknown. Major challenges in rare pediatric diseases include recruiting large numbers of patients and determining the significance and functional impacts of variants associated with disease within individuals or families. Whole exome sequencing (WES) is a powerful tool to identify coding variants that are associated with rare pediatric …
A Traders Guide To The Predictive Universe- A Model For Predicting Oil Price Targets And Trading On Them, Jimmie Harold Lenz
A Traders Guide To The Predictive Universe- A Model For Predicting Oil Price Targets And Trading On Them, Jimmie Harold Lenz
Doctor of Business Administration Dissertations
At heart every trader loves volatility; this is where return on investment comes from, this is what drives the proverbial “positive alpha.” As a trader, understanding the probabilities related to the volatility of prices is key, however if you could also predict future prices with reliability the world would be your oyster. To this end, I have achieved three goals with this dissertation, to develop a model to predict future short term prices (direction and magnitude), to effectively test this by generating consistent profits utilizing a trading model developed for this purpose, and to write a paper that anyone with …
Lead Poisoning In United States Children, Zeren Zhou
Lead Poisoning In United States Children, Zeren Zhou
Arts & Sciences Electronic Theses and Dissertations
We investigate factors related to blood lead levels of children ages 1 to 5 in the United States for the years 2007-2014. We use data from the National Health and Nutrition Examination Survey (NHANES). The goal is to explore predictors of lead in childrens' blood and to develop a multivariate model using as many predictors as possible. The analysis is conducted using SAS survey regression procedures that account for weighting, stratification, and clustering of the data.
Application Of Machine Learning To Mapping And Simulating Gene Regulatory Networks, Hien-Haw Liow
Application Of Machine Learning To Mapping And Simulating Gene Regulatory Networks, Hien-Haw Liow
Arts & Sciences Electronic Theses and Dissertations
This dissertation explores, proposes, and examines methods of applying modernmachine learning and Bayesian statistics in the quantitative and qualitative modeling of gene regulatory networks using high-throughput gene expression data. A semi-parametric Bayesian model based on random forest is developed to infer quantitative aspects of gene regulation relations; a parametric model is developed to predict geneexpression levels solely from genotype information. Simulation of network behavior is shown to complement regression analysis greatly in capturing the dynamics of gene regulatory networks. Finally, as an application and extension of novel approaches in gene expression analysis, new methods of discovering topological structure of gene …