Physical Sciences and Mathematics Commons^™

Generative Adversarial Game With Tailored Quantum Feature Maps For Enhanced Classification, Anais Sandra Nguemto Guiawa

Doctoral Dissertations

In the burgeoning field of quantum machine learning, the fusion of quantum computing and machine learning methodologies has sparked immense interest, particularly with the emergence of noisy intermediate-scale quantum (NISQ) devices. These devices hold the promise of achieving quantum advantage, but they grapple with limitations like constrained qubit counts, limited connectivity, operational noise, and a restricted set of operations. These challenges necessitate a strategic and deliberate approach to crafting effective quantum machine learning algorithms.

This dissertation revolves around an exploration of these challenges, presenting innovative strategies that tailor quantum algorithms and processes to seamlessly integrate with commercial quantum platforms. A …

Stressor: An R Package For Benchmarking Machine Learning Models, Samuel A. Haycock

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

Many discipline specific researchers need a way to quickly compare the accuracy of their predictive models to other alternatives. However, many of these researchers are not experienced with multiple programming languages. Python has recently been the leader in machine learning functionality, which includes the PyCaret library that allows users to develop high-performing machine learning models with only a few lines of code. The goal of the stressor package is to help users of the R programming language access the advantages of PyCaret without having to learn Python. This allows the user to leverage R’s powerful data analysis workflows, while simultaneously …

Benchmarking And Practical Evaluation Of Machine And Statistical Learning Methods In Credit Scoring: A Method Selection Perspective, Gwen Verbeck

UNLV Theses, Dissertations, Professional Papers, and Capstones

Predictive models are important tools used in all scientific fields. Machine learning (ML) algorithms and statistical models are widely used for decision-making because of their capability to tackle intricate and unique problems. In domains where data are high-dimensional and contain irrelevant and redundant features, ML algorithms are known to have superior performance over traditional (statistical) learning methods. However, researchers and analysts are often faced with a myriad of techniques to choose from, with no clear consensus on which will perform best for their specific task. Considering resource limitations, exhaustive exploration of all available methods is impractical and often fails to …