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 25 for “"automated Machine learning"”.
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MakeML : automated machine learning from data to predictions
… experience to easily and quickly create machine learning models that have competitive performance with models hand-built by trained data scientists. MakeML consists of a web-based application similar to a spreadsheet in which users select features and choose a target column to predict. …
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Mining Software Artifacts for use in Automated Machine Learning
Successfully implementing classical supervised machine learning pipelines requires that users have software engineering, machine learning, and domain experience. Machine learning libraries have helped along the first two dimensions by providing modular implementations of popular algorithms. …
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Automated Machine Learning for Predicting Trends in Time Series Data
… experimentation as well as domain specific and Machine Learning (ML) expert knowledge. This dissertation replicates TreNet experiments on the same datasets using a walk-forward validation method, which includes model update. The model is tested over multiple independent runs to evaluate model …
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Automated machine learning driven quality of service management in resource-constrained software defined networks
… a novel end-toend framework that uses deep learning models to facilitate real-time resource allocation in a resource-constrained network based on heuristics for traffic prioritisation. The deep learning models utilised by the framework are trained on data gathered from a community network …
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Collaborative, open, and automated data science
Data science and machine learning have already revolutionized many industries and organizations and are increasingly being used in an open-source setting to address important societal problems. However, there remain many challenges to developing predictive machine learning models in practice, such …
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Deep Mining : scaling Bayesian auto-tuning of data science pipelines
Within the automated machine learning movement, hyperparameter optimization has emerged as a particular focus. Researchers have introduced various search algorithms and open-source systems in order to automatically explore the hyperparameter space of machine learning methods. While these approaches …
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Evaluating Projections and Developing Projection Models for Daily Fantasy Basketball
… for NBA DFS contests and by developing machine learning models that produce competitive player projections.</p> <p>External sources are evaluated by constructing daily lineups based on the projections offered and evaluating those lineups in the context of all potential lineups, as well …
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A universal outbreak risk prediction tool
… a necessary. Existing prediction tools include machine learning-based tools and mathematical tools, which do not use machine learning. They both have disadvantages and advantages. My research goal is to develop an ideal outbreak prediction tool that has all the advantages at the same time and …
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Democratizing data science through interactive curation of ML pipelines
… yet such skills rarely coexist together. In Machine Learning, high-quality results are only attainable via mindful data preprocessing, hyperparameter tuning and model selection. Domain experts are often overwhelmed by such complexity, de-facto inhibiting a wider adoption of ML techniques in …
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Zephyr: a Data-Centric Framework for Predictive Maintenance of Wind Turbines
… In this thesis, we present Zephyr, a flexible machine learning framework for predictive maintenance of wind energy assets. Manual analysis of wind turbine data is difficult and time-consuming due to its volume, variety, and, most importantly, the need for quick detection of issues. Machine …
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Scalable Gaussian Processes: Advances in Iterative Methods and Pathwise Conditioning
… in hardware for massively-parallel computation, machine learning models trained on large amounts of data have become capable of accomplishing complex tasks, such as generating realistic images or maintaining conversations in natural language. However, the inability to know when they don't know …
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NANOFLUIDIC ISOLATION AND QUANTIFICATION OF SPECIFIC EXTRACELLULAR VESICLES AND MACHINE LEARNING ANALYSIS TO AID CLINICAL DECISION-MAKING
… LB dataset analysis, we developed a web-based, automated machine learning tool validated across 11 datasets. Its performance rivals or exceeds custom-designed algorithms, with capacity for improvement through historical data assimilation. A differential privacy algorithm which manages …
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Assessment of Individual Differences in Online Social Networks Using Machine Learning
… using offline behaviour, and I investigate if an automated machine learning system can measure the same psychological factors, only from observing the footprints of online behaviour, without observing any offline behaviour or any direct input from the individual. Prior research shows that …
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Data-Driven Human Decision Augmentation with Machine Learning
… in quantitative epistemology, reinforcement learning, predictive clustering, and automated machine learning, we introduce new mathematical formulations, develop novel machine learning models and algorithms, and provide experimental evaluations to demonstrate the practical utility of our …
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Quantitative Texture Analysis and Automated Segmentation of Patellar Tendon Sonographic Images on Collegiate Athletes
… These limitations can also be addressed through machine learning algorithms such as convolutional neural networks to automate image segmentations for determination of quantitative measures to determine injury. The primary objective of this research is to identify texture parameters the …
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Artificial intelligence in business analytics, capturing value with machine learning applications in financial services
… explores the strength and applicability of machine learning-based classifiers within the context of business analytics for data-driven decision making. The focus is on supervised binary classification on structured datasets, which are vastly present in relational databases across all …
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Software and Hardware Co-design for Efficient Neural Networks
… inference in the cloud. Finally, I show how automated machine learning techniques can be improved with hardware-awareness to produce efficient network architectures for emerging types of neural networks and new learning problem setups. Hardware-aware network architecture search (NAS) is able …
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Automatic detection of epileptic seizure onset and termination using intracranial EEG
… challenging as these are competing objectives. Automated machine learning systems provide a mechanism for dealing with these hurdles. Here we present and evaluate an algorithm for real-time seizure onset detection from IEEG using a machine-learning approach that permits a patient-specific …
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Enhancing gait recognition with 3D markerless motion capture
… on gait datasets. With the emergence of deep learning the focus of appearance-based methods has shifted, from the development of suitable gait representations to the optimisation of deep learning architectures using existing representations, such as Gait Energy Images (GEIs). However, advances …
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