Rozprawy doktorskie na temat „The learning space”
Utwórz poprawne odniesienie w stylach APA, MLA, Chicago, Harvard i wielu innych
Sprawdź 50 najlepszych rozpraw doktorskich naukowych na temat „The learning space”.
Przycisk „Dodaj do bibliografii” jest dostępny obok każdej pracy w bibliografii. Użyj go – a my automatycznie utworzymy odniesienie bibliograficzne do wybranej pracy w stylu cytowania, którego potrzebujesz: APA, MLA, Harvard, Chicago, Vancouver itp.
Możesz również pobrać pełny tekst publikacji naukowej w formacie „.pdf” i przeczytać adnotację do pracy online, jeśli odpowiednie parametry są dostępne w metadanych.
Przeglądaj rozprawy doktorskie z różnych dziedzin i twórz odpowiednie bibliografie.
Ameur, Foued ben Fredj. "Space-bounded learning algorithms /". Paderborn : Heinz Nixdorf Inst, 1996. http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&doc_number=007171235&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA.
Kiddle, Rebecca. "Learning outside the box : designing social learning space". Thesis, Oxford Brookes University, 2011. https://radar.brookes.ac.uk/radar/items/f7b36f17-cf4f-4590-8dd7-e6df3ecfc1d2/1/.
Ferreira, Paulo Victor Rodrigues. "SRML: Space Radio Machine Learning". Digital WPI, 2017. https://digitalcommons.wpi.edu/etd-dissertations/199.
Chardonnet, Lucile. "A Shared Learning Space inMidsommarkransen". Thesis, KTH, Arkitektur, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-223240.
Kumar, Shailesh. "Modular learning through output space decomposition /". Full text (PDF) from UMI/Dissertation Abstracts International, 2000. http://wwwlib.umi.com/cr/utexas/fullcit?p3004308.
Qian, Jing. "Unsupervised learning in high-dimensional space". Thesis, Boston University, 2014. https://hdl.handle.net/2144/12951.
In machine learning, the problem of unsupervised learning is that of trying to explain key features and find hidden structures in unlabeled data. In this thesis we focus on three unsupervised learning scenarios: graph based clustering with imbalanced data, point-wise anomaly detection and anomalous cluster detection on graphs. In the first part we study spectral clustering, a popular graph based clustering technique. We investigate the reason why spectral clustering performs badly on imbalanced and proximal data. We then propose the partition constrained minimum cut (PCut) framework based on a novel parametric graph construction method, that is shown to adapt to different degrees of imbalanced data. We analyze the limit cut behavior of our approach, and demonstrate the significant performance improvement through clustering and semi-supervised learning experiments on imbalanced data. [TRUNCATED]
Nichols, B. "Reinforcement learning in continuous state- and action-space". Thesis, University of Westminster, 2014. https://westminsterresearch.westminster.ac.uk/item/967w8/reinforcement-learning-in-continuous-state-and-action-space.
Saeed, Sabina, i Sabina Saeed. "Learning To Learn: A Look Into the Collaborative Learning Space". Thesis, The University of Arizona, 2017. http://hdl.handle.net/10150/625142.
Mackevicius, Emily Lambert. "Building a state space for song learning". Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/120871.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 159-177).
Song learning circuitry is thought to operate using a unique representation of each moment within each song syllable. Distinct timestamps for each moment in the song have been observed in the premotor cortical nucleus HVC, where neurons burst in sparse sequences. However, such sparse sequences are not present in very young birds, which sing highly variable syllables of random lengths. Furthermore, young birds learn by imitating a tutor song, and it was previously unclear precisely how the experience of hearing a tutor might shape auditory, motor, and evaluation pathways in the songbird brain. My thesis presents a framework for how these pathways may assemble during early learning, using simple neural mechanisms. I start with a neural network model for how premotor sequences may grow and split. This model predicts that the sequence-generating nucleus HVC would receive rhythmically patterned training inputs. I found such a signal when I recorded neurons that project to HVC. When juvenile birds sing, these neurons burst at the beginning of each syllable, and when the birds listen to a tutor, neurons burst at the rhythm of the tutor's song. Bursts marking the beginning of every tutor syllable could seed chains of sequential activity in HVC that could be used to generate the bird's own song imitation. I next used functional calcium imaging to characterize HVC sequences before and after tutor exposure. Analysis of these datasets led us to develop a new method for unsupervised detection of neural sequences. Using this method, I was able to observe neural sequences even prior to tutor exposure. Some of these sequences could be tracked as new syllables emerged after tutor exposure, and some sequences appeared to become coupled to the new syllables. In light of my new data, I expand on previous models of song learning to form a detailed hypothesis for how simple neural processes may perform song learning from start to finish.
by Emily Lambert Mackevicius.
Ph. D.
Bellocchi, Alberto. "Learning in the third space : a sociocultural perspective on learning with analogies". Queensland University of Technology, 2009. http://eprints.qut.edu.au/30136/.
Sabzpoushan, Maryam. "Play to learn : children learning and activity space". Thesis, KTH, Arkitektur, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-96485.
MAURO, ANA PAULA VIEIRA PEIXOTO. "DESIGN DE E-LEARNING: A SPACE UNDER CONSTRUCTION". PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2008. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=13051@1.
Esta pesquisa discute o papel do designer que atua no desenvolvimento de objetos educacionais virtuais voltados para o treinamento corporativo, inserido em estruturas produtivas de grande porte e com fins lucrativos. Nossa hipótese é a de que o design de e-learning não explora os recursos de hipermídia disponíveis de forma eficiente, em prol do engajamento do aluno. Nosso estudo se estrutura em três partes: primeiramente, dedicamo-nos a entender as estruturas produtivas de empresas com fins lucrativos que desenvolvem o e-learning corporativo e analisamos a atuação do designer nesse contexto. Em um segundo momento, nos debruçamos sobre o potencial oferecido pelo design de hipermídia, a partir das teorias do emotional design e do design de experiência, no sentido de se capturar e de se manter o interesse do usuário aluno. Finalmente, no último capítulo, empreendeu-se um estudo de casos exemplares encontrados na Internet, nos quais identificamos estratégias que visam a promoção de experiências imersivas. A partir dessas características, definimos as categorias que orientaram a análise de um curso a distância, com fins de treinamento corporativo, mediado pela Internet. Concluímos que o designer exerce um importante papel no que tange à usabilidade e ao tratamento informacional dos objetos educacionais virtuais. Entretanto, ao observar-se, por meio da perspectiva oferecida pelas categorias de análise elencadas a aplicação dos recursos utilizados no exemplo do e-learning corporativo, verifica-se que o designer subutiliza o potencial dos recursos de hipermídia hoje disponíveis, os quais poderiam promover um maior grau de envolvimento do aluno com o conteudo educacional apresentado.
This research discusses the role of the designer that works on the development of virtual educational objects regarding the corporate training, inserted in large size for-profit productive structures. Our hypothesis assumes that the e-learning design does not explore, effectively, the available hypermedia resources, for the student`s engagement. Our study id structured in three sections: first, we are dedicated to understanding the productive structures of the for-profit companies that develop the corporate e-learning and we analyze the action of the designer in this context. On a second moment, we debated the potential that hypermedia design offers, from the perspective of the emotional design and the experience design theories, in the sense of capturing and maintaining the user student`s attention. Finally, on the last chapter, a study of cases found on the internet was made, in which we identified strategies that look forward the promotion of immersive experiences. Departing from these features, we defined the categories that guide the analysis of a distance course with the purpose of corporate training through the internet. We conclude that the designer plays an important role in what concerns the usability and the informational treatment of the virtual educational objects. Meanwhile, observing, through the perspective offered by the listed categories of analysis, the application of the used resources in the example of the corporate e-learning, it is verified that the designer underuses the potential of the hypermedia resources available nowadays, which could promote a higher degree of involvement of the student with the educational content presented.
Haslam, Bryan (Bryan Todd). "Learning diseases from data : a disease space odyssey". Thesis, Massachusetts Institute of Technology, 2017. http://hdl.handle.net/1721.1/114002.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 253-280).
Recent commitments to enhance the use of data for learning in medicine provide the opportunity to apply instruments and abstractions from computational learning theory to systematize learning in medicine. The hope is to accelerate the rate at which we incorporate knowledge and improve healthcare quality. In this thesis, we work to bring further clarity to the ways in which computational learning theory can be applied to update the collective knowledge about diseases. Researchers continually study and learn about the complex nature of the human body. They summarize this knowledge with the best possible set of diseases and how those diseases relate to each other. We draw on computational learning theory to understand and broaden this form of collective learning. This mode of collective learning is regarded as unsupervised learning, as no disease labels are initially available. In unsupervised learning, variance is typically reduced to find an optimal function to organize the data. A significant challenge that remains is how to measure variance in the definition of diseases in a comprehensive way. Variance in the definition of a disease introduces a systematic error in both basic and clinical research. If measured, it would also be possible to use computers to efficiently minimize variance, providing a great opportunity for learning by utilizing medical data. In this thesis, we demonstrate that it is possible to estimate variance in the disease taxonomy, effectively estimating an error bar for the current definitions of diseases. We do so using the history of the disease taxonomy and comparing it with a variety of external data sets that relate diseases to attributes such as symptoms, drugs and genes. We demonstrate that variance can be significant over relatively short time periods. We further present methods for updating the disease taxonomy by reducing variance based on external disease data sets. This makes it possible to automatically incorporate information contained in disease data sets into the disease taxonomy. The approach also makes it possible to use expert information encoded in the taxonomy to systematically transfer knowledge and update other biomedical data sets that are often sparse (e.g. - symptoms associated with diseases). A natural question stemming from these results is how granular does data need to be to make improvements? For instance, is patient-level data necessary to enable learning at the macro level of disease? Or are there strategies to extract information from other kinds of data to alleviate the need for very granular data. We show that detailed, patient-level data is not necessarily needed to extract detailed biological data. We do so by comparing disease relationships learned from clinical trial metadata to disease relationships learned from a detailed genetic database and show we can achieve similar results. This result shows that we can use currently available data and take advantage of computational learning to improve disease learning, which suggests a new avenue to improving patient outcomes. By reducing variance within diseases using data available today, we can quickly update the space of diseases to be more precise. Precise diseases lead to better learning in other areas of medicine and ultimately improved healthcare quality.
by Bryan Haslam.
Ph. D.
Grönland, Axel, i Möllerstedt Viktor Eriksson. "Robust Reinforcement Learning in Continuous Action/State Space". Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-293879.
I detta projekt applicerar vi Robust Rein- forcement Learning (RRL) algoritmer, framtagna av Doya och Morimoto [1], [2], på reglerproblem. Målet var att träna en agent att balansera en pendel i det instabila jämviktsläget; det inverterade tillståndet. Vi undersökte prestandan hos regulatorer baserade på två value function approximators. Den ena är kvadratisk och den andra en Radial Basis Function neuralt nätverk. För att skapa robusthet så använder vi en metod som är ekvivalent med H∞ - reglering, som innebär att man introducerar en motståndare i reglersystemet. Genom att ändra pendelns massa efter träning, hoppas vi att som i [2] kunna visa att den förment robusta regulatorn klarar av denna störning bättre än sin icke-robusta mostvarighet. Detta var inte fallet. Vi lade även till en slumpmässig störsignal efter träning och utförde liknande tester, men lyckades inte visa robusthet i detta fall heller.
Kandidatexjobb i elektroteknik 2020, KTH, Stockholm
Ceylan, Hakan. "Using Reinforcement Learning in Partial Order Plan Space". Thesis, University of North Texas, 2006. https://digital.library.unt.edu/ark:/67531/metadc5232/.
Agiorgitis, Georgios, Mohamed Bennani, Mixalis Drakoularakos i Paul John McConnon. "Digital Wall : The University’s learning and information space". Thesis, Linnéuniversitetet, Institutionen för informatik (IK), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-62472.
This is a report in the Informatics course 5IK501 during the school year of 2016/2017.
Hawasly, Majd. "Policy space abstraction for a lifelong learning agent". Thesis, University of Edinburgh, 2014. http://hdl.handle.net/1842/9931.
Domert, Daniel. "Explorations of University Physics in Abstract Contexts : From de Sitter Space to Learning Space". Doctoral thesis, Uppsala universitet, Fysikundervisningen didaktik, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-7265.
Filip, Nikitas Metallinos Log, i Lipic Persson Sandra. "Learning in New Space : Knowledge Sourcing for Innovation in Northern Swedish New Space Companies". Thesis, Umeå universitet, Företagsekonomi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-173042.
Wenerstrom, Brent K. "Temporal Data Mining in a Dynamic Feature Space". BYU ScholarsArchive, 2006. https://scholarsarchive.byu.edu/etd/761.
Fägerstam, Emilia. "Space and Place : Perspectives on outdoor teaching and learning". Doctoral thesis, Linköpings universitet, Institutionen för beteendevetenskap och lärande, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-81318.
Denna avhandling syftar till att utforska lärares och elevers erfarenheter av, och uppfattningar om undervisning och lärande utomhus. Vidare syftar den till att undersöka vilken inverkan undervisning utomhus har på elevers resultat i biologi och matematik. Studierna är utförda i en svensk högstadieskola samt vid australiska miljöutbildningscentra. Det empiriska materialet består av elev- och lärarintervjuer samt enkäter och tester besvarade av elever. Det teoretiska ramverket utgår från platsteori samt ett lärandeperspektiv inkluderande tre dimensioner: innehållsliga, sociala och emotionella dimensioner av lärande. Resultaten visar att utemiljöns utvidgade fysiska rum har potential att förändra sociala relationer positivt och leda till ökat deltagande, samarbete, och kommunikation i ämnet Lärares erfarenhet var dock att det tog upp till tre månader innan eleverna var helt införstådda med utomhusundervisningens innebörd. Under den tiden var oordning i klassen ett hinder. Erfarenhet av specifika platser såsom lokal natur sågs av lärarna som väsentligt för elevernas platstillhörighet och miljöengagemang. Lärare vittnade dock om många elevers främlingskap inför lokala naturmiljöer. Kunskaper om naturen härstammade snarare från media än från egna erfarenheter och eleverna var ofta obekväma eller rädda i naturen. Matematik följt av språk var de ämnen som med störst regelbundenhet undervisades utomhus. I två delstudier jämfördes klassrumsundervisning med undervisning delvis utomhus i biologi och matematik. Resultaten visar på likvärdiga, eller mer utvecklade kunskaper som en följd av utomhusundervisning. En övergripande slutsats är att utomhusundervisningens möjligheter att samtidigt appellera till kognitiva, sociala och emotionella dimensioner av lärande kan konkretisera och vidga högstadieundervisningens teoretiskt inriktade innehåll samt bidra till långlivade episodiska minnen och en lust till lärande.
Benveniste, David 1977. "Cognitive conflict in learning three-dimensional space station structures". Thesis, Massachusetts Institute of Technology, 2004. http://hdl.handle.net/1721.1/26750.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
"September 2004."
Includes bibliographical references (p. 87-88).
(cont.) reached very high values early in the experiment and was significantly but slightly lower in FC than in GC. The target position relative to the subject's body did not affect performance, but subjects responded significantly faster when they were visually upright than when they were upside-down. Although alternative explanations cannot be ruled out, data collected and subjects' comments suggest that unlearning the GC cognitive map posed a significant challenge, and that subjects' knowledge of modules in GC, acquired earlier in the experiment, impeded their learning in FC, at least for the complex FC we used. Results of a Perspective Taking Ability test correlated weakly but significantly with TTR performance in GC, but not in FC. Other tests of spatial skills showed no significant correlation with performance. The effects of motion sickness susceptibility and of gender are also discussed. Supported by NASA Cooperative Agreement NCC 9-58 with the National Space Biomedical Research Institute.
Astronauts train on the ground in normal gravity, in replicas of the space station. Physical constraints force the configuration of these modules on the ground to be different from the configuration of the space station in flight. Based on descriptions of mishaps in human wayfinding (Jonsson 2002), it was hypothesized that the cognitive map of the space station formed from the replicas on the ground could be hard to unlearn. Could the resulting conflict with the actual configuration in flight explain why astronauts lack survey knowledge and often lose track of their orientation? Can they be trained using virtual reality to learn the correct configuration? What makes a configuration hard to learn or unlearn? We studied the ability to learn two realistic and polarized cubic modules in immersive virtual reality. Subjects (n=19) learned these modules first separately, then attached in two different configurations: first a "ground configuration" (GC), then a "flight configuration" (FC). The intrinsic visual verticals of both modules matched in GC, but not in FC, and walls at the interface between the modules were different in the two configurations. Subjects received guided tours of the modules and, through repeated trials, had to predict the location and orientation of one wall (the target), using the wall they were facing. The environment was pseudo-randomly rotated between trials. In the two module environments, subjects were set in the first module and had to place and orient the target wall in the second. The total time to respond to each trial (TTR) and the percent of correct responses (%-correct) were measured. The TTR decreased continuously with time within each virtual environment, but was significantly larger in FC than in GC. %-Correct
by David Benveniste.
S.M.
Gaynor, Dónal. "Space and Learning: A case study of their interaction". Thesis, Malmö högskola, Fakulteten för lärande och samhälle (LS), 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-32643.
Peker, Ender. "Campus As An Integrated Learning Environment: Learning In Campus Open Spaces". Master's thesis, METU, 2010. http://etd.lib.metu.edu.tr/upload/12612496/index.pdf.
informal learning&rsquo
. Therewithal, campus open spaces are one of the major areas where students prefer for their informal learning experiences. This thesis aims to search the influence of campus open space design on students&rsquo
learning experiences. Additionally, it argues that there is a strong relation between the learning and the space where learning action occurs. In doing this, it both covers a theoretical framework and a case study. Within the theoretical part, it discusses various learning theories with respect to the prominent principles for each theory. It reveals learning space design indicators which affects learning both in indoor and outdoor learning environments. In the case study, with the analysis of different sample areas from METU campus, the study both investigates the learning experiences actualized on campus open spaces and the triggering design indicators which enhance these experiences.
Li, Max Hongming. "Extension on Adaptive MAC Protocol for Space Communications". Digital WPI, 2018. https://digitalcommons.wpi.edu/etd-theses/1275.
Brückner, Jörg. "Automatic pattern recognition and learning for information systems". Thesis, University of Sussex, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.262632.
Glanville, Ranulph. "Architecture and space for thought". Thesis, Brunel University, 1988. http://bura.brunel.ac.uk/handle/2438/5018.
Seery, Kristin Kay. "GEOGRAPHIES OF LEARNING IN THE BLACKFEET NATION". UKnowledge, 2006. http://uknowledge.uky.edu/gradschool_theses/290.
Hagen, Stephanus Hendrikus Gerhardus ten. "Continuous state space Q-learning for control of nonlinear systems". [S.l. : Amsterdam : s.n.] ; Universiteit van Amsterdam [Host], 2001. http://dare.uva.nl/document/58530.
Qaed, Fatema. "Development of a supportive tool for participatory learning space design". Thesis, Northumbria University, 2015. http://nrl.northumbria.ac.uk/33885/.
Richards, Jason T. (Jason Todd) 1975. "Three-dimensional spatial learning in a virtual space station node". Thesis, Massachusetts Institute of Technology, 2000. http://hdl.handle.net/1721.1/69233.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Includes bibliographical references (p. 77-78).
Astronauts find it difficult to recognize their orientation while facing any of the viewing directions in 6-ported space station node modules. Our previous experiments tested the spatial memory of human subjects in 1-G in an analogous cubic virtual environment and showed that humans are able to learn to orient when instructed to imagine different body orientations while facing in two different directions. Can subjects do the task when facing in all 6 directions? Does training help? Does spatial memory depend on the direction of remembered targets relative to the body? Does performance depend on the subject's ability to rotate himself mentally and use imagery? How long is ability retained after training? 3D spatial learning was studied in two virtual cubic chambers, in which a picture of an animal was drawn on each wall. Through trial-by-trial exposures to a virtual chamber, subjects (n=24) had to memorize the spatial relationships among the 6 pictures around them and learn to predict the direction to a specific picture if they were facing any wall in any roll orientation. After learning in one chamber, the procedure was repeated in a second. Before being tested, subjects received computer-based instructions and practice. Half of subjects were taught to remember logical picture groupings (strategy), while the remaining (control) subjects were free to do the task as they saw fit. Subjects' retention of configurational knowledge (both chambers) and spatial ability (second chamber only, without feedback) were re-tested 1, 7, and 30 days after initial training. Response time (RT) and percent correct (% correct) learning curves were measured on all four days, while configurational knowledge was tested on the last three. All subjects ultimately learned to do the task within 36 trials in either test environment, but performed faster in the second environment than in the first (especially the strategy-trained group). The strategy group showed superior % correct and RT for above/behind targets and generally better configurational knowledge. Retention of configurational knowledge and spatial ability for both groups was good over 30 days. The subjects who reported using mental imagery (n=8) had higher scores on figure rotation tests and % correct for left/right targets. Performances by the control group on the experimental tasks were significantly correlated with those on conventional tests of field independence and 2/3D figure rotation ability. Strategy training helped those who had poorer mental rotation skills, and those who could not use mental imagery. Supported by NASA Cooperative Agreement NCC9-58 with the National Space Biomedical Research Institute, USA.
by Jason T. Richards.
S.M.
McHugh, Richard. "Educating 'gangsters' : social space, informal learning and becoming 'gang' involved". Thesis, Sheffield Hallam University, 2017. http://shura.shu.ac.uk/19163/.
Köhler, Thomas, Katrin Höhn, Martin Schmauder, Nina Kahnwald i Tanja Schilling. "The SIFA community as a virtual learning space in OSH". Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2015. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-181537.
Choate, James Edwin. "Learning from Frank Lloyd Wright". Thesis, Georgia Institute of Technology, 1985. http://hdl.handle.net/1853/23774.
Anagnostopoulou, Kyriaki. "Learning in third space : the nature of non-formal learning opportunities afforded to e-learning leaders in the workplace". Thesis, UCL Institute of Education (IOE), 2014. http://eprints.ioe.ac.uk/18424/.
Fan, Junchuan. "Modeling space-time activities and places for a smart space —a semantic approach". Diss., University of Iowa, 2017. https://ir.uiowa.edu/etd/5752.
Gibbings, Peter. "Experience of problem-based learning (PBL) in virtual space : a phenomenographical study". Queensland University of Technology, 2008. http://eprints.qut.edu.au/26423/.
Wenerstrom, Brent. "Temporal data mining in a dynamic feature space /". Diss., CLICK HERE for online access, 2006. http://contentdm.lib.byu.edu/ETD/image/etd1317.pdf.
Zhang, Xinhua, i xinhua zhang cs@gmail com. "Graphical Models: Modeling, Optimization, and Hilbert Space Embedding". The Australian National University. ANU College of Engineering and Computer Sciences, 2010. http://thesis.anu.edu.au./public/adt-ANU20100729.072500.
Ryan, Pius. "A case study of a networked learning community : the "third space"". Thesis, University of British Columbia, 2009. http://hdl.handle.net/2429/13858.
Guglielmo, Kennon. "A new learning controller for mechanical manipulators applied in Cartesian space". Thesis, Georgia Institute of Technology, 1989. http://hdl.handle.net/1853/17034.
Dare, Fadeke Taiye. "A novel methodology for e-learning space design in HEI campuses". Thesis, University of Wolverhampton, 2011. http://hdl.handle.net/2436/209931.
Chesters, William Robert. "Robot environment learning with a mixed-linear probabilistic state-space model". Thesis, University of Edinburgh, 2001. http://hdl.handle.net/1842/6567.
Jacobson, Rupert Daniel. "Exploring geographies of blindness : learning, reading and communicating in geographic space". Thesis, Queen's University Belfast, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.313978.
Martineau, Maxime. "Deep learning onto graph space : application to image-based insect recognition". Thesis, Tours, 2019. http://www.theses.fr/2019TOUR4024.
The goal of this thesis is to investigate insect recognition as an image-based pattern recognition problem. Although this problem has been extensively studied along the previous three decades, an element is to the best of our knowledge still to be experimented as of 2017: deep approaches. Therefore, a contribution is about determining to what extent deep convolutional neural networks (CNNs) can be applied to image-based insect recognition. Graph-based representations and methods have also been tested. Two attempts are presented: The former consists in designing a graph-perceptron classifier and the latter graph-based work in this thesis is on defining convolution on graphs to build graph convolutional neural networks. The last chapter of the thesis deals with applying most of the aforementioned methods to insect image recognition problems. Two datasets are proposed. The first one consists of lab-based images with constant background. The second one is generated by taking a ImageNet subset. This set is composed of field-based images. CNNs with transfer learning are the most successful method applied on these datasets
Kang, Qiwen. "UNSUPERVISED LEARNING IN PHYLOGENOMIC ANALYSIS OVER THE SPACE OF PHYLOGENETIC TREES". UKnowledge, 2019. https://uknowledge.uky.edu/statistics_etds/39.
Thomas, Rodney H. "Machine Learning for Exploring State Space Structure in Genetic Regulatory Networks". Diss., NSUWorks, 2018. https://nsuworks.nova.edu/gscis_etd/1053.
Liu, Mingxin. "A COMPARISON OF DEEP LEARNING AND CONVENTIONALALGORITHMS IN NARROW SPACE NAVIGATION". Case Western Reserve University School of Graduate Studies / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=case1592492127796282.
Toombs, Paul. "Reinforcement learning of visually guided spatial goal directed movement". Thesis, University of Stirling, 1997. http://hdl.handle.net/1893/2603.
Haworth, Avril. "The classroom as a heteroglossic space : dialogic talk in small group interaction". Thesis, Lancaster University, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.302366.