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 54 for “"data selection"”.
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Data selection in binary hypothesis testing
… are developed from probabilistic models for data. The design of the algorithms and their ultimate performance depend upon these assumed models. In certain situations, collecting or processing all available measurements may be inefficient or prohibitively costly. A potential technique to cope …
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Pushing the Limits of Active Data Selection with Gradient Matching
… of training on large, noisy, and imbalanced datasets have become increasingly pronounced—particularly in computer vision, where real-world data often contain labeling errors, occlusions, and redundancy. While large models can partially compensate by training exhaustively on massive datasets, …
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An Approach to Near Field Data Selection in Radio Frequency Identification
… to control access to their individual fields of data. This leaves them more available to unauthorized parties, and more prone to abuse. Here, then was undertaken a means to test a novel RFID card technology that allows overlays to be used for reliable, reversible data access settings. Similar to …
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Active learning based on a hybrid neural network modeller
… methods are investigated for selecting training data for the purpose of training neural networks. A new method called MIQR (Maximum Inter-Quartile Range) is proposed for effectively selecting a concise set of training data. In addition, the ensemble concept is introduced in this new method. Data …
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SEC Regulation of Corporate 10K Filing Dates: The Effect on Earnings Management and Market Recognition
… Balsam et al. study are either the result of the data selection process used in that study or the data selection processs used by Balsam et al. controlled for other market fluctuations not included in the current study. The results of this study suggest a positive relationship between earnings …
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Intelligent Flight Control Systems Using Adaptive Deep Neural Networks and Concurrent Learning-Based Design Methods
… batch-like training updates using a recorded data stack. The analysis focuses on the closed-loop performance improvements resulting from the use of optimum CL data-selection algorithms, which ensure that the recorded data stack maintains sufficient data diversity to provide a statistically …
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Visual Analytics and Interactive Machine Learning for Human Brain Data
… applying visualization techniques on human brain data for data exploration, quality control, and hypothesis discovery. It mainly consists of two parts: multi-modal data visualization and interactive machine learning. For multi-modal data visualization, a major challenge is how to integrate …
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Data-Centric Machine Learning for Speech and Audio
… is growing recognition of the importance of data-centric methods for building machine learning systems. Data-centric methods assume a fixed model and iterate over the data to improve system performance. This is in contrast to traditional model-centric approaches, which assume a fixed dataset …
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MOVING BLACK-BOXING TOWARDS STATISTICS: CASE STUDIES FROM AMERICAN FOOTBALL
… past decade, the explosion of publicly available data and off-the-shelf machine learning (ML) tools has popularized a common data science workflow: (1) obtain a dataset, (2) fit a black-box ML model, and (3) use its predictions. This workflow has become even more streamlined with LLMs—just upload …
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Exploring the Intersection of Physics Modeling and Representation Learning
… of solving arbitrary problems provided enough data. This thesis focuses on two primary directions: (1) Harnessing the power of deep learning for applications in fundamental physics and (2) using physicsinspired tools to improve and shed some light on otherwise large-scale, inscrutable black-box …
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Search for dark matter produced in association with a Higgs boson decaying to two photons in proton-proton collisions at 13 TeV with the CMS detector
… to two photons in proton-proton collision data collected at a center-of-mass energy of 13 TeV. The search is based on data collected in 2015 and 2016 by the CMS experiment at the CERN Large Hadron Collider. The analyzed data correspond to an integrated luminosity of 2.2/fb and 35.9/fb, …
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Hierarchical Bayesian Dataset Selection
… depends on access to large, high-quality datasets, which are often challenging to identify. To address this, we introduce <b>H</b>ierarchical <b>B</b>ayesian <b>D</b>ataset <b>S</b>election (<b>HBDS</b>), the first dataset selection algorithm that utilizes hierarchical Bayesian modeling, …
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ModelPred: A Framework for Predicting Trained Model from Training Data
… to understand the impact of changes in training data on a trained model. This is critical for building trust in various stages of a machine learning pipeline: from cleaning poor-quality samples and tracking important ones to be collected during data preparation, to calibrating uncertainty of …
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Police Use of Force Databases: Sources of Bias in Lethal Force Data Collection
… lethal force requires the collection of reliable data. Due to bias present in police-use-of-lethal-force databases, researchers typically triangulate using multiple data sources to compensate for this bias; however, triangulation is restricted when the bias present in each database is unknown. …
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Tree-based Data Replay for More Efficient LLM Continual Learning
… updating their knowledge bases with new data while retaining knowledge of prior information. This challenge is compounded by the considerable computational resources and time required to do so. This problem has been previously addressed using multiple approaches, including data replay, …
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Active Learning Under Limited Interaction with Data Labeler
… by identifying the most valuable unlabeled data points from a large pool. Traditional AL frameworks have two limitations: First, they perform data selection in a multi-round manner, which is time-consuming and impractical. Second, they usually assume that there are a small amount of labeled …
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Machine learning techniques for the health monitoring of rotating machinery in nuclear power plants
This thesis explores the development of data-driven and machine learning methods in application to the health monitoring of rotating plant items being used in the primary and secondary cycles of the Advanced Gas-cooled Reactor (AGR) nuclear power plants in the UK. The methods fall broadly into two …
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System Identification of a Nonlinear Flight Dynamics Model for a Small, Fixed-Wing UAV
… to atmospheric disturbances and decreased data quality from a cost-appropriate instrumentation system. These challenges result in difficulties in development of the model structure and parameter estimation. The small size may also limit the scope of flight test experiments and the …
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Deep Learning for online tagging of proton-proton collisions at the High-Luminosity LHC
… and one of the most prominent producers of big data. However, storing all this data is unfeasible; thus, a significant portion is filtered out using a trigger system. The upcoming High Luminosity upgrade is expected to increase data generation, demanding more efficient data processing …
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A context-aware approach for handling concept drift in classification
… main challenges associated with learning from data in dynamic environments. In particular, the description of the target concept is not static and may change over time under the influence of varying environmental conditions (i.e. varying context). Although many adaptive learning approaches have …
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