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 7419 for “"Machine learning"”.
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Architectures for machine learning and machine learning for architecture
Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2027-08-01
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Geospatial machine learning
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2024-03-01 without embargo terms
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Machine learning blocks
This work presents MLBlocks, a machine learning system that lets data scientists explore the space of modeling techniques in a very easy and efficient manner. We show how the system is very general in the sense that virtually any problem and dataset can be casted to use MLBlocks, and how it …
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Stable Machine Learning
… one of the most fundamental questions in Machine Learning, namely, how should the "learning" component in Machine Learning be done? For essentially the entire history of the field, ever since Mosteller and Tukey proposed the paradigm in 1968, the answer has remained constant: use …
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Machine learning ecosystem : implications for business strategy centered on machine learning
As interest for adopting machine learning as a core component of a business strategy increases, business owners face the challenge of integrating an uncertain and rapidly evolving technology into their organization, and depending on this for the success of their strategy. The field of Machine …
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Machine Learning Asset Allocation
… Risk Parity (HRP). El enfoque de HRP utiliza Machine Learning y teoría de grafos para construir un portafolio diversificado basado en la información contenida en la matriz de covarianza de los activos. El HRP se construye mediante la metodología de clustering jerárquico en donde las …
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REGULARIZATION ON MACHINE LEARNING
… neural networks have become a powerful tool for machine learning problems. However, overfitting frequently occurs. To achieve better generalization, many regularization methods were proposed to reduce overfitting. In this thesis, we propose a simple-yet-effective regularization method called …
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Machine learning in astronomy
… that will require new statistical inference and machine learning techniques for processing and analysis. Distinguishing between real objects and artefacts is one of the first steps in any transient science pipeline and, currently, is still carried out by humans - often leading to hand scanners …
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Guided Interactive Machine Learning
… the Crayons image classifier system and active learning. Currently Crayons provides no guidance to the user in what pixels should be labeled or when the task is complete. This work focuses on two main areas: 1) active learning for user guidance, and 2) accuracy estimation as a measure of …
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Socially guided machine learning
… interaction will be key to enabling robots and machines in general to learn new tasks from ordinary people (not experts in robotics or machine learning). Everyday people who need to teach their machines new things will find it natural for to rely on their interpersonal interaction skills. This …
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Solving Machine Learning Problems
Can a machine learn Machine Learning? This work trains a machine learning model to solve machine learning problems from a University undergraduate level course. We generate a new training set of questions and answers consisting of course exercises, homework, and quiz questions from MIT’s 6.036 …
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Adversarial machine learning in computer vision: attacks and defenses on machine learning models
Machine learning models, including neural networks, have gained great popularity in recent years. Deep neural networks are able to directly learn from raw data and can outperform traditional machine learning models. As a result, they have been increasingly used in a variety of application domains …
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Machine learning for electronic structure
Machine learning (ML) methods have recently experienced rising popularity in quantum chemistry as a means to bypass expensive electronic structure calculations, which are used to calculate quantum mechanical properties for systems of atoms. This has led to advances in a broad range of applications, …
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Privileged Machine Learning for Prediction
Machine learning for prediction suffers from asymmetric distribution, such as posterior information, future information and hidden information. With some additional information only available in training, how to learn a machine learning model with them remains a key challenge. Despite recent …
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High-throughput machine learning algorithms
The field of machine learning has become strongly compute driven, such that emerging research and applications require larger amounts of specialised hardware or smarter algorithms to advance beyond the state-of-the-art. This thesis develops specialised techniques and algorithms for a subset of …
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Distributed Support Vector Machine Learning
Support Vector Machines (SVMs) are used for a growing number of applications. A fundamental constraint on SVM learning is the management of the training set. This is because the order of computations goes as the square of the size of the training set. Typically, training sets of 1000 (500 positives …
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cell motility and machine learning
… for other cancers. The result shows that the machine learns the importance of cell count and cell infiltration and use the combination as an indicator for prognosis. We also applied the machine learning method on sperm classification and quantum many body problem. In sperm classification, we …
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Machine learning for network data
Contains fulltext : 137300.pdf (Publisher’s version ) (Open Access)
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