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Dissertations / Theses on the topic 'Domain adaption'

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Published: 4 June 2021

Last updated: 1 February 2022

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1

Pettersson, Harald. "Sentiment analysis and transfer learning using recurrent neural networks : an investigation of the power of transfer learning." Thesis, Linköpings universitet, Interaktiva och kognitiva system, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-161348.

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In the field of data mining, transfer learning is the method of transferring knowledge from one domain into another. Using reviews from prisjakt.se, a Swedish price comparison site, and hotels.com this work investigate how the similarities between domains affect the results of transfer learning when using recurrent neural networks. We test several different domains with different characteristics, e.g. size and lexical similarity. In this work only relatively similar domains were used, the same target function was sought and all reviews were in Swedish. Regardless, the results are conclusive; t

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2

Holm, Henrik. "Bidirectional Encoder Representations from Transformers (BERT) for Question Answering in the Telecom Domain. : Adapting a BERT-like language model to the telecom domain using the ELECTRA pre-training approach." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-301313.

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The Natural Language Processing (NLP) research area has seen notable advancements in recent years, one being the ELECTRA model which improves the sample efficiency of BERT pre-training by introducing a discriminative pre-training approach. Most publicly available language models are trained on general-domain datasets. Thus, research is lacking for niche domains with domain-specific vocabulary. In this paper, the process of adapting a BERT-like model to the telecom domain is investigated. For efficiency in training the model, the ELECTRA approach is selected. For measuring target- domain perfor

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3

Feyrer, Hubert. "System administration training in the virtual unix lab an e-learning system with diagnosis via a domain specific language as base for an architecture for tutorial assistance and user adaption." Aachen Shaker, 2008. http://d-nb.info/992564581/04.

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4

Sundström, Johan. "Sentiment analysis of Swedish reviews and transfer learning using Convolutional Neural Networks." Thesis, Uppsala universitet, Avdelningen för systemteknik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-339066.

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Sentiment analysis is a field within machine learning that focus on determine the contextual polarity of subjective information. It is a technique that can be used to analyze the "voice of the customer" and has been applied with success for the English language for opinionated information such as customer reviews, political opinions and social media data. A major problem regarding machine learning models is that they are domain dependent and will therefore not perform well for other domains. Transfer learning or domain adaption is a research field that study a model's ability of transferring k

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Feyrer, Hubert [Verfasser]. "System Administration Training in the Virtual Unix Lab : An e-learning system with diagnosis via a domain specific language as base for an architecture for tutorial assistance and user adaption / Hubert Feyrer." Aachen : Shaker, 2009. http://d-nb.info/1161309985/34.

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6

Collins, Gordon. "Invariant adaptive domain methods." Thesis, University of Bristol, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.245511.

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7

Hake, Michael James. "Spectroscopic Characterization of the Interaction of Nck Domains with the Epidermal Growth Factor Receptor Juxtamembrane Domain." Case Western Reserve University School of Graduate Studies / OhioLINK, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=case1207340174.

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8

Sandu, Oana. "Domain adaptation for summarizing conversations." Thesis, University of British Columbia, 2011. http://hdl.handle.net/2429/33932.

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The goal of summarization in natural language processing is to create abridged and informative versions of documents. A popular approach is supervised extractive summarization: given a training source corpus of documents with sentences labeled with their informativeness, train a model to select sentences from a target document and produce an extract. Conversational text is challenging to summarize because it is less formal, its structure depends on the modality or domain, and few annotated corpora exist. We use a labeled corpus of meeting transcripts as the source, and attempt to summa

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Ye, Lei. "Adaptive frequency-domain access techniques." Thesis, University of York, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.479512.

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10

Htike, Kyaw Kyaw. "Domain adaptation for pedestrian detection." Thesis, University of Leeds, 2014. http://etheses.whiterose.ac.uk/7290/.

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Object detection is an essential component of many computer vision systems. The increase in the amount of collected digital data and new applications of computer vision have generated a demand for object detectors for many different types of scenes digitally captured in diverse settings. The appearance of objects captured across these different scenarios can vary significantly, causing readily available state-of-the-art object detectors to perform poorly in many of the scenes. One solution is to annotate and collect labelled data for each new scene and train a scene-specific object detector th

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11

Albakour, M.-Dyaa. "Adaptive domain modelling for information retrieval." Thesis, University of Essex, 2012. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.573703.

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12

Yang, Baoyao. "Distribution alignment for unsupervised domain adaptation: cross-domain feature learning and synthesis." HKBU Institutional Repository, 2018. https://repository.hkbu.edu.hk/etd_oa/556.

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In recent years, many machine learning algorithms have been developed and widely applied in various applications. However, most of them have considered the data distributions of the training and test datasets to be similar. This thesis concerns on the decrease of generalization ability in a test dataset when the data distribution is different from that of the training dataset. As labels may be unavailable in the test dataset in practical applications, we follow the effective approach of unsupervised domain adaptation and propose distribution alignment methods to improve the generalization abil

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13

Xu, Jiaolong. "Domain adaptation of deformable part-based models." Doctoral thesis, Universitat Autònoma de Barcelona, 2015. http://hdl.handle.net/10803/290266.

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La detecció de vianants és crucial per als sistemes d’assistència a la conducció (ADAS). Disposar d’un classificador precís és fonamental per a un detector de vianants basat en visió. Al entrenar un classificador, s’assumeix que les característiques de les dades d’entrenament segueixen la mateixa distribució de probabilitat que la de les dades de prova. Tot i això, a la pràctica, aquesta assumpció pot no complir-se per diferents causes. En aquests casos, en la comunitat de visió per computador és cada cop més comú utilitzar tècniques que permeten adaptar els classificadors existents del

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14

Herndon, Nic. "Domain adaptation algorithms for biological sequence classification." Diss., Kansas State University, 2016. http://hdl.handle.net/2097/35242.

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Doctor of Philosophy<br>Department of Computing and Information Sciences<br>Doina Caragea<br>The large volume of data generated in the recent years has created opportunities for discoveries in various fields. In biology, next generation sequencing technologies determine faster and cheaper the exact order of nucleotides present within a DNA or RNA fragment. This large volume of data requires the use of automated tools to extract information and generate knowledge. Machine learning classification algorithms provide an automated means to annotate data but require some of these data to be manuall

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Shu, Le. "Graph and Subspace Learning for Domain Adaptation." Diss., Temple University Libraries, 2015. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/363757.

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Computer and Information Science<br>Ph.D.<br>In many practical problems, given that the instances in the training and test may be drawn from different distributions, traditional supervised learning can not achieve good performance on the new domain. Domain adaptation algorithms are therefore designed to bridge the distribution gap between training (source) data and test (target) data. In this thesis, I propose two graph learning and two subspace learning methods for domain adaptation. Graph learning methods use a graph to model pairwise relations between instances and then minimize the domain

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Shahabuddin, Sharmeen. "Compressed Domain Spatial Adaptation of H264 Videos." Thesis, University of Ottawa (Canada), 2010. http://hdl.handle.net/10393/28787.

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A gigantic amount of multimedia contents is readily available for an ever growing consumer base at present due to advances in video coding technology and standardization along with rapid improvements of storage capacity and computing power. However, today's pervasive media environment which includes heterogeneous terminals and networks presents a serious obstacle in achieving seamless access to these contents. Storing individual content in several formats taking into account a wide variety of possible user preferences and resource constraints or adapting the content on the fly by cascaded deco

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Palm, Myllylä Johannes. "Domain Adaptation for Hypernym Discovery via Automatic Collection of Domain-Speciﬁc Training Data". Thesis, Linköpings universitet, Institutionen för datavetenskap, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-157693.

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Identifying semantic relations in natural language text is an important component of many knowledge extraction systems. This thesis studies the task of hypernym discovery, i.e discovering terms that are related by the hypernymy (is-a) relation. Speciﬁcally, this thesis explores how state-of-the-art methods for hypernym discovery perform when applied in speciﬁc language domains. In recent times, state-of-the-art methods for hypernym discovery are mostly made up by supervised machine learning models that leverage distributional word representations such as word embeddings. These models require l

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Rubino, Raphaël. "Traduction automatique statistique et adaptation à un domaine spécialisé." Phd thesis, Université d'Avignon, 2011. http://tel.archives-ouvertes.fr/tel-00879945.

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Nous avons observé depuis plusieurs années l'émergence des approches statistiques pour la traduction automatique. Cependant, l'efficacité des modèles construits est soumise aux variabilités inhérentes au langage naturel. Des études ont montré la présence de vocabulaires spécifique et général composant les corpus de textes de domaines spécialisés. Cette particularité peut être prise en charge par des ressources terminologiques comme les lexiques bilingues.Toutefois, nous pensons que si le vocabulaire est différent entre des textes spécialisés ou génériques, le contenu sémantique et la structure

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19

Tian, Tian. "Domain Adaptation and Model Combination for the Annotation of Multi-source, Multi-domain Texts." Thesis, Paris 3, 2019. http://www.theses.fr/2019PA030003.

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Internet propose aujourd’hui aux utilisateurs de services en ligne de commenter, d’éditer et de partager leurs points de vue sur différents sujets de discussion. Ce type de contenu est maintenant devenu la ressource principale pour les analyses d’opinions sur Internet. Néanmoins, à cause des abréviations, du bruit, des fautes d’orthographe et toutes autres sortes de problèmes, les outils de traitements automatiques des langues, y compris les reconnaisseurs d’entités nommées et les étiqueteurs automatiques morphosyntaxiques, ont des performances plus faibles que sur les textes bien-formés (Ritt

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20

Selvaggi, Kevin. "Synthetic-to-Real Domain Adaptation for Autonomous Driving." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2020.

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Questa tesi rappresenta il risultato di un tirocinio svolto presso il reparto di test di Siemens Industry Software NV Leuven, in Belgio. Il primo obiettivo è stato quello di avere una visione generale sul settore della guida autonoma e delle relative tecnologie: si presenta quindi un'analisi della letteratura. Il campo è stato quindi ristretto ai riconoscitori di oggetti 2D che utilizzano sensori automotive come dispositivi di input. Dopo uno studio dello stato dell'arte di architetture di reti neurali e dei corrispondenti dataset usati per l'allenamento in questo settore, la domanda di

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21

Zhai, Yiming. "Adaptive log domain filters using floating gate transistors." College Park, Md. : University of Maryland, 2004. http://hdl.handle.net/1903/2132.

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Thesis (M.S.) -- University of Maryland, College Park, 2004.<br>Thesis research directed by: Dept. of Electrical and Computer Engineering . Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.

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Shah, Darsh J. (Darsh Jaidip). "Multi-source domain adaptation with mixture of experts." Thesis, Massachusetts Institute of Technology, 2019. https://hdl.handle.net/1721.1/121741.

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Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 35-37).<br>We propose a mixture-of-experts approach for unsupervised domain adaptation from multiple sources. The key idea is to explicitly capture the relationship between a target example and different source domains. This relationship, expressed by a point-to-set metric, determines how to combine predictors trained on various domains. The metric is learned in an unsupervised fashion using m

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23

Thornström, Johan. "Domain Adaptation of Unreal Images for Image Classification." Thesis, Linköpings universitet, Datorseende, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-165758.

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Deep learning has been intensively researched in computer vision tasks like im-age classification. Collecting and labeling images that these neural networks aretrained on is labor-intensive, which is why alternative methods of collecting im-ages are of interest. Virtual environments allow rendering images and automaticlabeling, which could speed up the process of generating training data and re-duce costs.This thesis studies the problem of transfer learning in image classificationwhen the classifier has been trained on rendered images using a game engine andtested on real images. Th

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Sopova, Oleksandra. "Domain adaptation for classifying disaster-related Twitter data." Kansas State University, 2017. http://hdl.handle.net/2097/35388.

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Master of Science<br>Department of Computing and Information Sciences<br>Doina Caragea<br>Machine learning is the subfield of Artificial intelligence that gives computers the ability to learn without being explicitly programmed, as it was defined by Arthur Samuel - the American pioneer in the field of computer gaming and artificial intelligence who was born in Emporia, Kansas. Supervised Machine Learning is focused on building predictive models given labeled training data. Data may come from a variety of sources, for instance, social media networks. In our research, we use Twitter data, spec

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Meftah, Sara. "Neural Transfer Learning for Domain Adaptation in Natural Language Processing." Thesis, université Paris-Saclay, 2021. http://www.theses.fr/2021UPASG021.

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Les méthodes d’apprentissage automatique qui reposent sur les Réseaux de Neurones (RNs) ont démontré des performances de prédiction qui s'approchent de plus en plus de la performance humaine dans plusieurs applications du Traitement Automatique de la Langue (TAL) qui bénéficient de la capacité des différentes architectures des RNs à généraliser à partir des régularités apprises à partir d'exemples d'apprentissage. Toutefois, ces modèles sont limités par leur dépendance aux données annotées. En effet, pour être performants, ces modèles neuronaux ont besoin de corpus annotés de taille importante

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26

Marchand, Morgane. "Domaines et fouille d'opinion : une étude des marqueurs multi-polaires au niveau du texte." Thesis, Paris 11, 2015. http://www.theses.fr/2015PA112026/document.

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Cette thèse s’intéresse à l’adaptation d’un classifieur statistique d’opinion au niveau du texte d’un domaine à un autre. Cependant, nous exprimons notre opinion différemment selon ce dont nous parlons. Un même mot peut ne pas désigner pas la même chose ou bien ne pas avoir la même connotation selon le thème de la discussion. Si ces mots ne sont pas détectés, ils induiront des erreurs de classification.Nous appelons donc marqueurs multi-polaires des mots ou bigrammes dont la présence indique une certaine polarité du texte entier, différente selon le domaine du texte. Cette thèse est consacrées

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27

Rottmann, Matthias [Verfasser]. "Adaptive Domain Decomposition Multigrid for Lattice QCD / Matthias Rottmann." Wuppertal : Universitätsbibliothek Wuppertal, 2016. http://d-nb.info/1093603240/34.

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28

Donati, Lorenzo. "Domain Adaptation through Deep Neural Networks for Health Informatics." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2017. http://amslaurea.unibo.it/14888/.

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The PreventIT project is an EU Horizon 2020 project aimed at preventing early functional decline at younger old age. The analysis of causal links between risk factors and functional decline has been made possible by the cooperation of several research institutes' studies. However, since each research institute collects and delivers different kinds of data in different formats, so far the analysis has been assisted by expert geriatricians whose role is to detect the best candidates among hundreds of fields and offer a semantic interpretation of the values. This manual data harmonization approac

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Adamo, Ronald C. "Adaptive windows via Kalman filtering in the spectral domain." Thesis, Monterey, California. Naval Postgraduate School, 1991. http://hdl.handle.net/10945/27934.

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30

Panayiotou, Stephen. "A domain independent adaptive imaging system for visual inspection." Thesis, University of Greenwich, 1995. http://gala.gre.ac.uk/8696/.

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Computer vision is a rapidly growing area. The range of applications is increasing very quickly, robotics, inspection, medicine, physics and document processing are all computer vision applications still in their infancy. All these applications are written with a specific task in mind and do not perform well unless there under a controlled environment. They do not deploy any knowledge to produce a meaningful description of the scene, or indeed aid in the analysis of the image. The construction of a symbolic description of a scene from a digitised image is a difficult problem. A symbolic interp

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Manamasa, Krishna Himaja. "Domain adaptation from 3D synthetic images to real images." Thesis, Blekinge Tekniska Högskola, Institutionen för datavetenskap, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-19303.

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Background. Domain adaptation is described as, a model learning from a source data distribution and performing well on the target data. This concept, Domain adaptation is applied to assembly-line production tasks to perform an automatic quality inspection. Objectives. The aim of this master thesis is to apply this concept of 3D domain adaptation from synthetic images to real images. It is an attempt to bridge the gap between different domains (synthetic and real point cloud images), by implementing deep learning models that learn from synthetic 3D point cloud (CAD model images) and perform wel

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Liu, Ye. "Application of Convolutional Deep Belief Networks to Domain Adaptation." The Ohio State University, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=osu1397728737.

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Chinea, Ríos Mara. "Advanced techniques for domain adaptation in Statistical Machine Translation." Doctoral thesis, Universitat Politècnica de València, 2019. http://hdl.handle.net/10251/117611.

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[ES] La Traducción Automática Estadística es un sup-campo de la lingüística computacional que investiga como emplear los ordenadores en el proceso de traducción de un texto de un lenguaje humano a otro. La traducción automática estadística es el enfoque más popular que se emplea para construir estos sistemas de traducción automáticos. La calidad de dichos sistemas depende en gran medida de los ejemplos de traducción que se emplean durante los procesos de entrenamiento y adaptación de los modelos. Los conjuntos de datos empleados son obtenidos a partir de una gran variedad de fuentes y en mucho

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Radhakrishnan, Saieshwar. "Domain Adaptation of IMU sensors using Generative Adversarial Networks." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-286821.

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Autonomous vehicles rely on sensors for a clear understanding of the environment and in a heavy duty truck, the sensors are placed at multiple locations like the cabin, chassis and the trailer in order to increase the field of view and reduce the blind spot area. Usually, these sensors perform best when they are stationary relative to the ground, hence large and fast movements, which are quite common in a truck, may lead to performance reduction, erroneous data or in the worst case, a sensor failure. This enforces a need to validate the sensors before using them for making life-critical decisi

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Bajic, Vladan. "DESIGN AND IMPLEMENTATION OF AN ADAPTIVE NOISE CANCELING SYSTEM IN WAVELET TRANSFORM DOMAIN." University of Akron / OhioLINK, 2005. http://rave.ohiolink.edu/etdc/view?acc_num=akron1132784671.

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Kofler, Michael. "GYF domains a class of proline rich ligand binding adaptor domains /." kostenfrei, 2007. http://www.diss.fu-berlin.de/2007/261/index.html.

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37

Chandler, Brendan. "The SH2 Domain-Containing Adaptor Protein SHD Reversibly Binds the CRKL-SH2 Domain and Knockdown of shdb Impairs Zebrafish Eye Development." ScholarWorks @ UVM, 2018. https://scholarworks.uvm.edu/graddis/878.

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The adaptor protein CT10-Regulator of Kinase (CRK) and the closely related CRK-Like (CRKL) are adaptor proteins that play important roles in many signaling pathways regulating cell proliferation and cell motility. A notable example is their required role in Reelin signaling during development of the laminated structures of the vertebrate central nervous system, including the cerebral cortex, cerebellum, hippocampus, and retina. As adaptors, CRK/CRKL are important in coupling phosphotyrosine signaling to G protein activity to regulate both cell proliferation and changes in the actin cytoskeleto

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Cardace, Adriano. "Learning Features Across Tasks and Domains." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2020. http://amslaurea.unibo.it/20050/.

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The absence of in-domain labeled data hinders the applicability of powerful deep neural networks. Unsupervised Domain Adaptation (UDA) methods have emerged to exploit such models even when labeled data is not available in the target domain. All these techniques aim to reduce the distribution shift problem that afflicts these models when trained on one dataset and tested in a different one. However, most of the works, do not consider relationships among tasks to further boost performances. In this thesis, we study a recent method called AT/DT (Across Tasks Domain Transfer), that seeks to apply

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Shell, Jethro. "Fuzzy transfer learning." Thesis, De Montfort University, 2013. http://hdl.handle.net/2086/8842.

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The use of machine learning to predict output from data, using a model, is a well studied area. There are, however, a number of real-world applications that require a model to be produced but have little or no data available of the specific environment. These situations are prominent in Intelligent Environments (IEs). The sparsity of the data can be a result of the physical nature of the implementation, such as sensors placed into disaster recovery scenarios, or where the focus of the data acquisition is on very defined user groups, in the case of disabled individuals. Standard machine learnin

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Vázquez, Bermúdez David. "Domain Adaptation of Virtual and Real Worlds for Pedestrian Detection." Doctoral thesis, Universitat Autònoma de Barcelona, 2013. http://hdl.handle.net/10803/125977.

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La detección de peatones es clave para muchas aplicaciones como asistencia al conductor, video vigilancia o multimedia. Los mejores detectores se basan en clasificadores basados en modelos de apariencia entrenados con ejemplos anotados. Sin embargo, el proceso de anotación es una tarea intensiva y subjetiva cuando es llevada a cabo por personas. Por ello, vale la pena minimizar la intervención humana en dicha tarea mediante el uso de herramientas computacionales como los mundos virtuales porque con ellos podemos obtener anotaciones variadas y precisas de forma rápida. Sin embargo, el uso de es

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Ruberg, Anders. "Frequency Domain Link Adaptation for OFDM-based Cellular Packet Data." Thesis, Linköping University, Department of Electrical Engineering, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-6328.

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<p>In order to be competitive with emerging mobile systems and to satisfy the ever growing request for higher data rates, the 3G consortium, 3rd Generation Partnership Project (3GPP), is currently developing concepts for a long term evolution (LTE) of the 3G standard. The LTE-concept at Ericsson is based on Orthogonal Frequency Division Multiplexing (OFDM) as downlink air interface. OFDM enables the use of frequency domain link adaptation to select the most appropriate transmission parameters according to current channel conditions, in order to maximize the throughput and maintain the delay at

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Patel, Bhagirath. "Model reference adaptive control system using frequency domain performance specifications." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape8/PQDD_0023/MQ52071.pdf.

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Werner, Manuel. "Adaptive wavelet frame domain decomposition methods for elliptic operator equations /." Berlin : Logos-Verl, 2009. http://d-nb.info/99721984X/04.

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Birkett, A. Neil (Alexander Neil) Carleton University Dissertation Engineering Electrical. "A SAW based transversal filter for adaptive time domain equalization." Ottawa, 1988.

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Vaudrey, Michael A. "A novel approach to multiple reference frequency domain adaptive control." Thesis, This resource online, 1996. http://scholar.lib.vt.edu/theses/available/etd-08292008-063731/.

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XIAO, MIN. "Generalized Domain Adaptation for Sequence Labeling in Natural Language Processing." Diss., Temple University Libraries, 2016. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/391382.

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Computer and Information Science<br>Ph.D.<br>Sequence labeling tasks have been widely studied in the natural language processing area, such as part-of-speech tagging, syntactic chunking, dependency parsing, and etc. Most of those systems are developed on a large amount of labeled training data via supervised learning. However, manually collecting labeled training data is too time-consuming and expensive. As an alternative, to alleviate the issue of label scarcity, domain adaptation has recently been proposed to train a statistical machine learning model in a target domain where there is no eno

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LaMaire, Richard O. "Robust time and frequency domain estimation methods in adaptive control." Thesis, Massachusetts Institute of Technology, 1987. http://hdl.handle.net/1721.1/14795.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1987.<br>MICROFICHE COPY AVAILABLE IN ARCHIVES AND ENGINEERING.<br>Supported, in part, by the NASA Ames & Langley Research Centers, the Office of Naval Research, and the National Science Foundation.<br>Bibliography: v. 2, leaves 334-337.<br>by Richard Orville LaMaire.<br>Ph.D.

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Xu, Brian(Brian W. ). "Combating fake news with adversarial domain adaptation and neural models." Thesis, Massachusetts Institute of Technology, 2019. https://hdl.handle.net/1721.1/121689.

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This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019<br>Cataloged from student-submitted PDF version of thesis.<br>Includes bibliographical references (pages 77-80).<br>Factually incorrect claims on the web and in social media can cause considerable damage to individuals and societies by misleading them. As we enter an era where it is easier than ever to disseminate "fake news" and

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Alrobaea, Roobaea. "Generating a domain-specific inspection method through an adaptive framework." Thesis, University of East Anglia, 2015. https://ueaeprints.uea.ac.uk/59678/.

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Many recent innovations and inventions have contributed to rapid technological development, which in turn have produced a wide variety of products that have had a major impact on many businesses in several different domains. These products have their own contextual attributes that have made their usability evaluation, by using traditional usability evaluation methods (UEMs), all the more critical. Almost all previous usability studies have used the Heuristic Evaluation (HE) and User Testing (UT) methods; however, the majority of such studies have described these methods as being not directly a

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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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