Thematische Bibliographien / Learning with Limited Data / Dissertationen
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Dissertationen zum Thema „Learning with Limited Data“

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Veröffentlicht am 18. Mai 2024

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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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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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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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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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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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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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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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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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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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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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Rücklé, Andreas [Verfasser], Iryna [Akademischer Betreuer] Gurevych, Jonathan [Akademischer Betreuer] Berant, and Goran [Akademischer Betreuer] Glavaš. "Representation Learning and Learning from Limited Labeled Data for Community Question Answering / Andreas Rücklé ; Iryna Gurevych, Jonathan Berant, Goran Glavaš." Darmstadt : Universitäts- und Landesbibliothek, 2021. http://d-nb.info/1236344472/34.

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Omstedt, Fredrik. "A deep reinforcement learning approach to the problem of golf using an agent limited by human data." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-277832.

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In the sport of golf, the use of statistics has become prominent as a way of understanding and improving golfers’ golf swings. Even though swing data is easily accessible thanks to a variety of technological tools, it is not always clear how to use this data, especially for amateur golfers. This thesis investigates the feasibility of using reinforcement learning in conjunction with a golfer’s data to play golf, something that could provide insight on how the golfer can improve. Specifically, a Dueling Double Deep Q Network agent and a Multi Pass Deep Q Network agent were trained and evaluated
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Sherwin, Jason. "A computational approach to achieve situational awareness from limited observations of a complex system." Diss., Georgia Institute of Technology, 2010. http://hdl.handle.net/1853/33955.

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At the start of the 21st century, the topic of complexity remains a formidable challenge in engineering, science and other aspects of our world. It seems that when disaster strikes it is because some complex and unforeseen interaction causes the unfortunate outcome. Why did the financial system of the world meltdown in 2008-2009? Why are global temperatures on the rise? These questions and other ones like them are difficult to answer because they pertain to contexts that require lengthy descriptions. In other words, these contexts are complex. But we as human beings are able to observe and re
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Harár, Pavol. "Klasifikace audia hlubokým učením s limitovanými zdroji dat." Doctoral thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2019. http://www.nusl.cz/ntk/nusl-408054.

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Standardní postupy diagnózy dysfonie klinickým logopedem mají své nevýhody, především tu, že je tento proces velmi subjektivní. Nicméně v poslední době získala popularitu automatická objektivní analýza stavu mluvčího. Vědci úspěšně založili své metody na různých algoritmech strojového učení a ručně vytvořených příznacích. Tyto metody nejsou bohužel přímo škálovatelné na jiné poruchy hlasu, samotný proces tvorby příznaků je pracný a také náročný z hlediska financí a talentu. Na základě předchozích úspěchů může přístup založený na hlubokém učení pomoci překlenout některé problémy se škálovatelno
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Giaretta, Lodovico. "Pushing the Limits of Gossip-Based Decentralised Machine Learning." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-253794.

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Recent years have seen a sharp increase in the ubiquity and power of connected devices, such as smartphones, smart appliances and smart sensors. These de- vices produce large amounts of data that can be extremely precious for training larger, more advanced machine learning models. Unfortunately, it is some- times not possible to collect and process these datasets on a central system, due either to their size or to the growing privacy requirements of digital data handling.To overcome this limit, researchers developed protocols to train global models in a decentralised fashion, exploiting the co
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Pettersson, Christoffer. "Investigating the Correlation Between Marketing Emails and Receivers Using Unsupervised Machine Learning on Limited Data : A comprehensive study using state of the art methods for text clustering and natural language processing." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-189147.

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The goal of this project is to investigate any correlation between marketing emails and their receivers using machine learning and only a limited amount of initial data. The data consists of roughly 1200 emails and 98.000 receivers of these. Initially, the emails are grouped together based on their content using text clustering. They contain no information regarding prior labeling or categorization which creates a need for an unsupervised learning approach using solely the raw text based content as data. The project investigates state-of-the-art concepts like bag-of-words for calculating term
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Young, William Albert II. "LEARNING RATES WITH CONFIDENCE LIMITS FOR JET ENGINE MANUFACTURING PROCESSES AND PART FAMILIES FROM NOISY DATA." Ohio University / OhioLINK, 2005. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1131637106.

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Trávníčková, Kateřina. "Interaktivní segmentace 3D CT dat s využitím hlubokého učení." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2020. http://www.nusl.cz/ntk/nusl-432864.

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This thesis deals with CT data segmentation using convolutional neural nets and describes the problem of training with limited training sets. User interaction is suggested as means of improving segmentation quality for the models trained on small training sets and the possibility of using transfer learning is also considered. All of the chosen methods help improve the segmentation quality in comparison with the baseline method, which is the use of automatic data specific segmentation model. The segmentation has improved by tens of percents in Dice score when trained with very small datasets. T
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Yang, Xuan, and 楊譞. "Budget-limited data disambiguation." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2013. http://hdl.handle.net/10722/196458.

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The problem of data ambiguity exists in a wide range of applications. In this thesis, we study “cost-aware" methods to alleviate the data ambiguity problems in uncertain databases and social-tagging data. In database applications, ambiguous (or uncertain) data may originate from data integration and measurement error of devices. These ambiguous data are maintained by uncertain databases. In many situations, it is possible to “clean", or remove, ambiguities from these databases. For example, the GPS location of a user is inexact due to measurement error, but context information (e.g., what a
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Wang, Chen. "Global investigation of marine atmospheric boundary layer rolls using Sentinel-1 SAR data." Thesis, Ecole nationale supérieure Mines-Télécom Atlantique Bretagne Pays de la Loire, 2020. http://www.theses.fr/2020IMTA0203.

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Cette thèse exploite les données globales du radar à ouverture synthétique (SAR) Sentinel-1 (S-1) pour l'étude des rouleaux de la couche limite atmosphérique marine (MABL). Un modèle basé sur l'apprentissage en profondeur a été développé pour identifier automatiquement les rouleaux à partir des images massives S-1 WV. L'évaluation prouve que des rouleaux plus nombreux et plus clairs sont visibles à un angle d'incidence plus grand avec une limitation à des vitesses de vent très basses et lorsque la direction du vent est perpendiculaire à l'antenne SAR. Au delà de cela, les énormes données condu
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Senecal, Joshua G. "Length-limited data transformation and compression /." For electronic version search Digital dissertations database. Restricted to UC campuses. Access is free to UC campus dissertations, 2005. http://uclibs.org/PID/11984.

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Pople, Andrew James. "Value-based maintenance using limited data." Thesis, University of Newcastle Upon Tyne, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.391958.

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Hsu, Bo-June (Bo-June Paul). "Language Modeling for limited-data domains." Thesis, Massachusetts Institute of Technology, 2009. http://hdl.handle.net/1721.1/52796.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2009.<br>This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Cataloged from student submitted PDF version of thesis.<br>Includes bibliographical references (p. 99-109).<br>With the increasing focus of speech recognition and natural language processing applications on domains with limited amount of in-domain training data, enhanced system performance often relies on approaches involving mod
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Chang, Eric I.-Chao. "Improving wordspotting performance with limited training data." Thesis, Massachusetts Institute of Technology, 1995. http://hdl.handle.net/1721.1/38056.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1995.<br>Includes bibliographical references (leaves 149-155).<br>by Eric I-Chao Chang.<br>Ph.D.
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Zama, Ramirez Pierluigi <1992&gt. "Deep Scene Understanding with Limited Training Data." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amsdottorato.unibo.it/9815/1/zamaramirez_pierluigi_tesi.pdf.

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Scene understanding by a machine is a challenging task due to the profound variety of nature. Nevertheless, deep learning achieves impressive results in several scene understanding tasks such as semantic segmentation, depth estimation, or optical flow. However, these kinds of approaches need a large amount of labeled data, leading to massive manual annotations, which are incredibly tedious and expensive to collect. In this thesis, we will focus on understanding a scene through deep learning with limited data availability. First of all, we will tackle the problem of the lack of data for seman
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Caprioli, Francesco. "Optimal fiscal policy, limited commitment and learning." Doctoral thesis, Universitat Pompeu Fabra, 2009. http://hdl.handle.net/10803/7396.

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Esta tesis trata sobre cómo la autoridad fiscal debe fijar los impuestos distorsivos de manera óptima. El capítulo 1 analiza el problema de la política fiscal cuando el gobierno tiene un incentivo a hacer default con su deuda externa. El capítulo 2 trata sobre el problema de la política fiscal cuando los agentes no conocen cómo el gobierno fija las tasas impositivas. La principal conclusión que obtengo es que, en ambos contextos, el resultado de suavidad de las tasas, que es estándar en la literatura de imposición óptima, se rompe. Cuando los gobiernos no tienen una tecnología de compromiso,
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O'Farrell, Michael Robert. "Estimating persistence of fished populations with limited data /." For electronic version search Digital dissertations database. Restricted to UC campuses. Access is free to UC campus dissertations, 2005. http://uclibs.org/PID/11984.

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Dowd, Michael. "Assimilation of data into limited-area coastal models." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/nq24773.pdf.

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McLaughlin, N. R. "Robust multimodal person identification given limited training data." Thesis, Queen's University Belfast, 2013. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.579747.

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Abstract This thesis presents a novel method of audio-visual fusion, known as multi- modal optimal feature fusion (MOFF), for person identification where both the speech and facial modalities may be corrupted, and there is a lack of prior knowl- edge about the corruption. Furthermore, it is assumed there is a limited amount of training data for each modality (e.g., a short training speech segment and a single training facial image for each person). A new multimodal feature rep- resentation and a modified cosine similarity are introduced for combining and comparing bimodal features with limited
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Fattah, Polla. "Behaviour classification for temporal data with limited readings." Thesis, University of Nottingham, 2017. http://eprints.nottingham.ac.uk/44677/.

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Classifying items using temporal data, i.e. several readings of the same attribute in different time points, has many applications in the real world. The pivotal question which motivates this study is: ''Is it possible to quantify behavioural change in temporal data? And what is the best reference point to compare the behaviour change with?". The focus of this study will be in applications in economics such as playing many rounds of public goods games and share price moves in the stock market. There are many methods for classifying temporal data and many methods for measuring the change of ite
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Ding, Silin. "Freeway Travel Time Estimation Using Limited Loop Data." University of Akron / OhioLINK, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=akron1205288596.

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Li, Jiawei. "Person re-identification with limited labeled training data." HKBU Institutional Repository, 2018. https://repository.hkbu.edu.hk/etd_oa/541.

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With the growing installation of surveillance video cameras in both private and public areas, it is an immediate requirement to develop intelligent video analysis system for the large-scale camera network. As a prerequisite step of person tracking and person retrieval in intelligent video analysis, person re-identification, which targets in matching person images across camera views is an important topic in computer vision community and has been received increasing attention in the recent years. In the supervised learning methods, the person re-identification task is formulated as a classifica
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Anderson, Christopher. "BANDWIDTH LIMITED 320 MBPS TRANSMITTER." International Foundation for Telemetering, 1996. http://hdl.handle.net/10150/607635.

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International Telemetering Conference Proceedings / October 28-31, 1996 / Town and Country Hotel and Convention Center, San Diego, California<br>With every new spacecraft that is designed comes a greater density of information that will be stored once it is in operation. This, coupled with the desire to reduce the number of ground stations needed to download this information from the spacecraft, places new requirements on telemetry transmitters. These new transmitters must be capable of data rates of 320 Mbps and beyond. Although the necessary bandwidth is available for some non-bandwidth
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Watkins, Andrew B. "AIRS: a resource limited artificial immune classifier." Master's thesis, Mississippi State : Mississippi State University, 2001. http://library.msstate.edu/etd/show.asp?etd=etd-11052001-102048.

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Zhang, Yi. "Learning with Limited Supervision by Input and Output Coding." Research Showcase @ CMU, 2012. http://repository.cmu.edu/dissertations/156.

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In many real-world applications of supervised learning, only a limited number of labeled examples are available because the cost of obtaining high-quality examples is high. Even with a relatively large number of labeled examples, the learning problem may still suffer from limited supervision as the complexity of the prediction function increases. Therefore, learning with limited supervision presents a major challenge to machine learning. With the goal of supervision reduction, this thesis studies the representation, discovery and incorporation of extra input and output information in learning.
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GIOBERGIA, FLAVIO. "Machine learning with limited label availability: algorithms and applications." Doctoral thesis, Politecnico di Torino, 2023. https://hdl.handle.net/11583/2976594.

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Van, Niekerk Daniel Rudolph. "Automatic speech segmentation with limited data / by D.R. van Niekerk." Thesis, North-West University, 2009. http://hdl.handle.net/10394/3978.

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The rapid development of corpus-based speech systems such as concatenative synthesis systems for under-resourced languages requires an efficient, consistent and accurate solution with regard to phonetic speech segmentation. Manual development of phonetically annotated corpora is a time consuming and expensive process which suffers from challenges regarding consistency and reproducibility, while automation of this process has only been satisfactorily demonstrated on large corpora of a select few languages by employing techniques requiring extensive and specialised resources. In this work we cons
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Nagy, Arnold B. "Priority area performance and planning areas with limited biological data." Thesis, University of Sheffield, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.425193.

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Dowler, John D. "Using Neural Networks with Limited Data to Estimate Manufacturing Cost." Ohio University / OhioLINK, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1211293606.

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Szotten, David. "Limited data problems in X-ray and polarized light tomography." Thesis, University of Manchester, 2011. https://www.research.manchester.ac.uk/portal/en/theses/limited-data-problems-in-xray-and-polarized-light-tomography(5bc153b4-7344-4a62-9879-e23cc3d60b2d).html.

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We present new reconstruction results and methods for limited data problems in photoelastic tomography. We begin with a survey of the current state of x-ray tomography. Discussing the Radon transform and its inversion we also consider some stability results for reconstruction in Sobolev spaces. We describe certain limited data problems and ways to tackle these, in particular the Two Step Hilbert reconstruction method. We then move on to photoelastic tomography, where we make use of techniques from scalar tomography to develop new methods for photoelastic tomographic reconstruction. We present
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Qu, Lizhen [Verfasser], and Gerhard [Akademischer Betreuer] Weikum. "Sentiment analysis with limited training data / Lizhen Qu. Betreuer: Gerhard Weikum." Saarbrücken : Saarländische Universitäts- und Landesbibliothek, 2013. http://d-nb.info/1053680104/34.

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Mehdawi, Nader. "Monitoring for Underdetermined Underground Structures during Excavation Using Limited Sensor Data." Master's thesis, University of Central Florida, 2013. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/5670.

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A realistic field monitoring application to evaluate close proximity tunneling effects of a new tunnel on an existing tunnel is presented. A blind source separation (BSS)-based monitoring framework was developed using sensor data collected from the existing tunnel while the new tunnel was excavated. The developed monitoring framework is particularly useful to analyze underdetermined systems due to insufficient sensor data for explicit input force-output deformation relations. The analysis results show that the eigen-parameters obtained from the correlation matrix of raw sensor data can be u
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White, Susan Mary. "Sediment yield estimation from limited data sets : a Philippines case study." Thesis, University of Exeter, 1992. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.332300.

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Don, Michael, and Tom Harkins. "Achieving High Resolution Measurements Within Limited Bandwidth Via Sensor Data Compression." International Foundation for Telemetering, 2012. http://hdl.handle.net/10150/581447.

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ITC/USA 2012 Conference Proceedings / The Forty-Eighth Annual International Telemetering Conference and Technical Exhibition / October 22-25, 2012 / Town and Country Resort & Convention Center, San Diego, California<br>The U.S. Army Research Laboratory (ARL) is developing an onboard instrument and telemetry system to obtain measurements of the 30mm MK310 projectile's in-flight dynamics. The small size, high launch acceleration, and extremely high rates of this projectile create many design challenges. Particularly challenging is the high spin rate which can reach 1400 Hz at launch. The bandwid
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Abidin, Mohamed Roseli bin Zainal. "Hydrological and hydraulic sensitivity analyses for flood modelling with limited data." Thesis, University of Birmingham, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.707174.

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46

Fisher, Emily. "Tools for assessing data-limited fisheries and communicating stock status information." Thesis, Fisher, Emily (2012) Tools for assessing data-limited fisheries and communicating stock status information. PhD thesis, Murdoch University, 2012. https://researchrepository.murdoch.edu.au/id/eprint/14881/.

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This PhD study was focused on developing and exploring tools for assessing the status of data-limited fish stocks. A management strategy evaluation (MSE) framework was developed to explore the effectiveness of alternative strategies for managing fish stocks for which sufficient data are available to allow a catch curve-based assessment, but which lack the reliable time series data on catches and/or catch per unit of effort required for developing an integrated age-structured fishery model. Explorations using the operating model of this framework indicated that, particularly for demersal fi
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Säfdal, Joakim. "Data-Driven Engine Fault Classification and Severity Estimation Using Interpolated Fault Modes from Limited Training Data." Thesis, Linköpings universitet, Fordonssystem, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-173916.

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Today modern vehicles are expected to be safe, environmentally friendly, durable and economical. Monitoring the health of the vehicle is therefore more important than ever. As the complexity of vehicular systems increases the need for efficient monitoring methods has increased as well. Traditional methods of deriving models for the systems are today not as efficient as the complexity of the systems increases the time and skill needed to implement the models. An alternative is data driven methods where a collection of data associated with the behavior of the system is used to draw conclusions o
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48

Dlamini, Luleka. "Exploring the potential of using remote sensing data to model agricultural systems in data-limited areas." Master's thesis, Faculty of Science, 2020. http://hdl.handle.net/11427/32239.

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Crop models (CMs) can be a key component in addressing issues of global food security as they can be used to monitor and improve crop production. Regardless of their wide utilization, the employment of these models, particularly in isolated and rural areas, is often limited by the lack of reliable input data. This data scarcity increases uncertainties in model outputs. Nevertheless, some of these uncertainties can be mitigated by integrating remotely sensed data into the CMs. As such, increasing efforts are being made globally to integrate remotely sensed data into CMs to improve their overall
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49

Barrère, Killian. "Architectures de Transformer légères pour la reconnaissance de textes manuscrits anciens." Electronic Thesis or Diss., Rennes, INSA, 2023. http://www.theses.fr/2023ISAR0017.

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En reconnaissance d’écriture manuscrite, les architectures Transformer permettent de faibles taux d’erreur, mais sont difficiles à entraîner avec le peu de données annotées disponibles. Dans ce manuscrit, nous proposons des architectures Transformer légères adaptées aux données limitées. Nous introduisons une architecture rapide basée sur un encodeur Transformer, et traitant jusqu’à 60 pages par seconde. Nous proposons aussi des architectures utilisant un décodeur Transformer pour inclure l’apprentissage de la langue dans la reconnaissance des caractères. Pour entraîner efficacement nos archit
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Sanakoyeu, Artsiom [Verfasser], and Björn [Akademischer Betreuer] Ommer. "Visual Representation Learning with Limited Supervision / Artsiom Sanakoyeu ; Betreuer: Björn Ommer." Heidelberg : Universitätsbibliothek Heidelberg, 2021. http://d-nb.info/1231632488/34.

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