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Thematische Bibliographien / Learning with Limited Data

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Dissertationen

Auswahl der wissenschaftlichen Literatur zum Thema „Learning with Limited Data“

Autor: Grafiati

Veröffentlicht am 18. Mai 2024

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Dissertationen zum Thema "Learning with Limited Data"

1

Chen, Si. "Active Learning Under Limited Interaction with Data Labeler." Thesis, Virginia Tech, 2021. http://hdl.handle.net/10919/104894.

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Active learning (AL) aims at reducing labeling effort 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 data points available in the same domain as the data in the unlabeled pool. In this thesis, we initiate the study of one-round active learning to solve the first issue. We propose DULO, a general framework for one-round setting based on the notion of

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2

Dvornik, Mikita. "Learning with Limited Annotated Data for Visual Understanding." Thesis, Université Grenoble Alpes (ComUE), 2019. http://www.theses.fr/2019GREAM050.

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La capacité des méthodes d'apprentissage profond à exceller en vision par ordinateur dépend fortement de la quantité de données annotées disponibles pour la formation. Pour certaines tâches, l'annotation peut être trop coûteuse et demander trop de travail, devenant ainsi le principal obstacle à une meilleure précision. Les algorithmes qui apprennent automatiquement à partir des données, sans supervision humaine, donnent de bien pires résultats que leurs homologues entièrement supervisés. Il y a donc une forte motivation à travailler sur des méthodes efficaces d'apprentissage avec des annotatio

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3

Moskvyak, Olga. "Learning from limited annotated data for re-identification problem." Thesis, Queensland University of Technology, 2021. https://eprints.qut.edu.au/226866/1/Olga_Moskvyak_Thesis.pdf.

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The project develops machine learning methods for the re-identification task, which is matching images from the same category in a database. The thesis proposes approaches to reduce the influence of two critical challenges in image re-identification: pose variations that affect the appearance of objects and the need to annotate a large dataset to train a neural network. Depending on the domain, these challenges occur to a different extent. Our approach demonstrates superior performance on several benchmarks for people, cars, and animal categories.

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4

Xian, Yongqin [Verfasser]. "Learning from limited labeled data - Zero-Shot and Few-Shot Learning / Yongqin Xian." Saarbrücken : Saarländische Universitäts- und Landesbibliothek, 2020. http://d-nb.info/1219904457/34.

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5

Eriksson, Håkan. "Clustering Generic Log Files Under Limited Data Assumptions." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-189642.

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Complex computer systems are often prone to anomalous or erroneous behavior, which can lead to costly downtime as the systems are diagnosed and repaired. One source of information for diagnosing the errors and anomalies are log files, which are often generated in vast and diverse amounts. However, the log files' size and semi-structured nature makes manual analysis of log files generally infeasible. Some automation is desirable to sift through the log files to find the source of the anomalies or errors. This project aimed to develop a generic algorithm that could cluster diverse log files in a

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6

Boman, Jimmy. "A deep learning approach to defect detection with limited data availability." Thesis, Umeå universitet, Institutionen för fysik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-173207.

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In industrial processes, products are often visually inspected for defects inorder to verify their quality. Many automated visual inspection algorithms exist, and in many cases humans still perform the inspections. Advances in machine learning have showed that deep learning methods lie at the forefront of reliability and accuracy in such inspection tasks. In order to detect defects, most deep learning methods need large amounts of training data to learn from. This makes demonstrating such methods to a new customer problematic, since such data often does not exist beforehand, and has to be gath

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7

Guo, Zhenyu. "Data famine in big data era : machine learning algorithms for visual object recognition with limited training data." Thesis, University of British Columbia, 2014. http://hdl.handle.net/2429/46412.

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Big data is an increasingly attractive concept in many fields both in academia and in industry. The increasing amount of information actually builds an illusion that we are going to have enough data to solve all the data driven problems. Unfortunately it is not true, especially for areas where machine learning methods are heavily employed, since sufficient high-quality training data doesn't necessarily come with the big data, and it is not easy or sometimes impossible to collect sufficient training samples, which most computational algorithms depend on. This thesis mainly focuses on dealing si

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8

Ayllon, Clemente Irene [Verfasser]. "Towards natural speech acquisition: incremental word learning with limited data / Irene Ayllon Clemente." Bielefeld : Universitätsbibliothek Bielefeld, 2013. http://d-nb.info/1077063458/34.

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9

Chang, Fengming. "Learning accuracy from limited data using mega-fuzzification method to improve small data set learning accuracy for early flexible manufacturing system scheduling." Saarbrücken VDM Verlag Dr. Müller, 2005. http://d-nb.info/989267156/04.

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10

Tania, Zannatun Nayem. "Machine Learning with Reconfigurable Privacy on Resource-Limited Edge Computing Devices." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-292105.

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Distributed computing allows effective data storage, processing and retrieval but it poses security and privacy issues. Sensors are the cornerstone of the IoT-based pipelines, since they constantly capture data until it can be analyzed at the central cloud resources. However, these sensor nodes are often constrained by limited resources. Ideally, it is desired to make all the collected data features private but due to resource limitations, it may not always be possible. Making all the features private may cause overutilization of resources, which would in turn affect the performance of the who

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