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 40 for “"Interpretable Machine Learning"”.
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Interpretable Machine Learning Methods for Landslide Analysis
… to them is urgent. In this project, we use machine learning to computationally study landslide detection, the likelihood of past landslides occurrence, and landslide susceptibility, or risk, in the Mocoa region. The region’s geographical and climate features make it elusive to remote-sensing …
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Interpretable machine learning methods for stroke prediction
Machine learning has long been touted as the next big tool, revolutionizing scientific endeavors as well as impacting industries like retail and finance. Naturally, there is much interest in the potential of next improving healthcare. However, using traditional machine learning approaches in this …
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Efficient and Faithful Algorithms for Interpretable Machine Learning
As deep learning models continue to grow in complexity and scale, the demand for interpretable machine learning (ML) methods becomes increasingly critical across a wide range of applications. This thesis addresses the challenges of interpreting deep neural networks (DNNs) by designing efficient and …
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Toward Efficient Automation of Interpretable Machine Learning Boosting
<p>Developing efficient automated methods for Interpretable Machine Learning (IML) is an important and long-term goal in the field of Artificial Intelligence. Currently the Machine Learning landscape is dominated by Neural Networks (NNs) and Support Vector Machines (SVMs), models which are often …
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Causal and system-theoretic approaches to interpretable machine learning
While machine learning algorithms are achieving increasingly impressive levels of decision-making and predictive performance, the way such algorithms operate is also becoming more and more inscrutable. As more decisions are delegated to complex systems that are not susceptible to any form of human …
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Interpretable machine learning methods with applications to health care
… In this thesis, we improve and propose some interpretable machine learning methods by using modern optimization. We also use two examples to illustrate how interpretable machine learning methods help to solve problems in health care. The first part of this thesis is about interpretable …
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Interpretability by Design: New Interpretable Machine Learning Models and Methods
<p>As machine learning models are playing increasingly important roles in many real-life scenarios, interpretability has become a key issue for whether we can trust the predictions made by these models, especially when we are making some high-stakes decisions. Lack of transparency has long been a …
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Interactive and interpretable machine learning models for human machine collaboration
… successful collaborations between humans and machine learning models by harnessing the relative strength to accomplish what neither can do alone. Machine learning techniques and humans have skills that complement each other - machine learning techniques are good at computation on data at the …
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STUDYING PRODUCT REVIEWS USING SENTIMENT ANALYSIS BASED ON INTERPRETABLE MACHINE LEARNING
… This thesis carries out a natural-language based machine learning study to analyze the relationship from e-commerce big data of product reviews and ratings. Towards answering this relationship question using natural-language-processing (NLP), we first employ data-driven sentiment analysis to …
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Interpretable Machine Learning for Prediction and Avoidance of Disruptions in Tokamak Plasmas
… decades of research make this problem ripe for machine learning-based prediction and control, yet it is often difficult to explain how these data-driven algorithms make particular predictions. This thesis demonstrates the novel application of data-driven methods to address this issue via two …
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Detecting food safety risks and human tracking using interpretable machine learning methods/
Black box machine learning methods have allowed researchers to design accurate models using large amounts of data at the cost of interpretability. Model interpretability not only improves user buy-in, but in many cases provides users with important information. Especially in the case of the …
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Accelerating Catalytic Materials Discovery for Sustainable Nitrogen Transformations by Interpretable Machine Learning
Computational chemistry and machine learning approaches are combined to understand the mechanisms, derive activity trends, and ultimately to search for active electrocatalysts for the electrochemical oxidation of ammonia (AOR) and nitrate reduction (NO3RR). Both re- actions play vital roles within …
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New interpretable machine learning techniques and an application to stroke prediction in atrial fibrillation patients
Building interpretable and accurate models are attracting more and more interest in the machine learning community. In this thesis, we developed an interpretable machine learning algorithm called SBRL and we built an interpretable and statistically more accurate model for predicting strokes for …
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Revamping Manufacturing Systems: Utilization of Data Driven Models, Interpretable Machine Learning, and Data-Product Stakeholder Flow Analysis
… systems, improving the interpretability of machine learning models, and analyzing stakeholder flow to develop effective manufacturing data products. The first study involves modeling an industrial coffee roaster system. Using production data collected during the roasting process and multiple …
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Interpretable Modeling of Immunotherapy Response Factors
… and whole-exome seqeuencing (WES) data into an interpretable machine learning model and investigates genetic factors that may separate responders from nonresponders. We discovered that both data types contribute to response separation and that certain gene sets may be especially important …
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Optical imaging with machine learning for the automated characterization of micro- and nanoscale devices
… First, the dissertation presents a novel interpretable machine learning technique for defect detection and classification in noisy optical images of semiconductor wafer die. This solution is designed to solve the imbalanced data-set classification problem for noisy images with some feature …
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