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Dissertations / Theses on the topic 'Hyperparameter selection and optimization'

Author: Grafiati

Published: 25 May 2024

Last updated: 31 July 2025

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1

Ndiaye, Eugene. "Safe optimization algorithms for variable selection and hyperparameter tuning." Thesis, Université Paris-Saclay (ComUE), 2018. http://www.theses.fr/2018SACLT004/document.

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Le traitement massif et automatique des données requiert le développement de techniques de filtration des informations les plus importantes. Parmi ces méthodes, celles présentant des structures parcimonieuses se sont révélées idoines pour améliorer l’efficacité statistique et computationnelle des estimateurs, dans un contexte de grandes dimensions. Elles s’expriment souvent comme solution de la minimisation du risque empirique régularisé s’écrivant comme une somme d’un terme lisse qui mesure la qualité de l’ajustement aux données, et d’un terme non lisse qui pénalise les solutions complexes. C

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Ndiaye, Eugene. "Safe optimization algorithms for variable selection and hyperparameter tuning." Electronic Thesis or Diss., Université Paris-Saclay (ComUE), 2018. http://www.theses.fr/2018SACLT004.

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Le traitement massif et automatique des données requiert le développement de techniques de filtration des informations les plus importantes. Parmi ces méthodes, celles présentant des structures parcimonieuses se sont révélées idoines pour améliorer l’efficacité statistique et computationnelle des estimateurs, dans un contexte de grandes dimensions. Elles s’expriment souvent comme solution de la minimisation du risque empirique régularisé s’écrivant comme une somme d’un terme lisse qui mesure la qualité de l’ajustement aux données, et d’un terme non lisse qui pénalise les solutions complexes. C

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3

Thornton, Chris. "Auto-WEKA : combined selection and hyperparameter optimization of supervised machine learning algorithms." Thesis, University of British Columbia, 2014. http://hdl.handle.net/2429/46177.

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Many different machine learning algorithms exist; taking into account each algorithm's set of hyperparameters, there is a staggeringly large number of possible choices. This project considers the problem of simultaneously selecting a learning algorithm and setting its hyperparameters. Previous works attack these issues separately, but this problem can be addressed by a fully automated approach, in particular by leveraging recent innovations in Bayesian optimization. The WEKA software package provides an implementation for a number of feature selection and supervised machine learning algorithms

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Bertrand, Quentin. "Hyperparameter selection for high dimensional sparse learning : application to neuroimaging." Electronic Thesis or Diss., université Paris-Saclay, 2021. http://www.theses.fr/2021UPASG054.

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Grâce à leur caractère non invasif et leur excellente résolution temporelle, la magnéto- et l'électroencéphalographie (M/EEG) sont devenues des outils incontournables pour observer l'activité cérébrale. La reconstruction des signaux cérébraux à partir des enregistrements M/EEG peut être vue comme un problème inverse de grande dimension mal posé. Les estimateurs typiques des signaux cérébraux se basent sur des problèmes d'optimisation difficiles à résoudre, composés de la somme d'un terme d'attache aux données et d'un terme favorisant la parcimonie. À cause du paramètre de régularisation notoir

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Thomas, Janek [Verfasser], and Bernd [Akademischer Betreuer] Bischl. "Gradient boosting in automatic machine learning: feature selection and hyperparameter optimization / Janek Thomas ; Betreuer: Bernd Bischl." München : Universitätsbibliothek der Ludwig-Maximilians-Universität, 2019. http://d-nb.info/1189584808/34.

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Nakisa, Bahareh. "Emotion classification using advanced machine learning techniques applied to wearable physiological signals data." Thesis, Queensland University of Technology, 2019. https://eprints.qut.edu.au/129875/9/Bahareh%20Nakisa%20Thesis.pdf.

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This research contributed to the development of advanced feature selection model, hyperparameter optimization and temporal multimodal deep learning model to improve the performance of dimensional emotion recognition. This study adopts different approaches based on portable wearable physiological sensors. It identified best models for feature selection and best hyperparameter values for Long Short-Term Memory network and how to fuse multi-modal sensors efficiently for assessing emotion recognition. All methods of this thesis collectively deliver better algorithms and maximize the use of miniatu

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Klein, Aaron [Verfasser], and Frank [Akademischer Betreuer] Hutter. "Efficient bayesian hyperparameter optimization." Freiburg : Universität, 2020. http://d-nb.info/1214592961/34.

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8

Gousseau, Clément. "Hyperparameter Optimization for Convolutional Neural Networks." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-272107.

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Training algorithms for artificial neural networks depend on parameters called the hyperparameters. They can have a strong influence on the trained model but are often chosen manually with trial and error experiments. This thesis, conducted at Orange Labs Lannion, presents and evaluates three algorithms that aim at solving this task: a naive approach (random search), a Bayesian approach (Tree Parzen Estimator) and an evolutionary approach (Particle Swarm Optimization). A well-known dataset for handwritten digit recognition (MNIST) is used to compare these algorithms. These algorithms are also

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Firmin, Thomas. "Parallel hyperparameter optimization of spiking neural networks." Electronic Thesis or Diss., Université de Lille (2022-....), 2025. http://www.theses.fr/2025ULILB004.

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Les Réseaux de Neurones Artificiels (RNAs) sont des modèles prédictifs permettant de résoudre certaines tâches complexes par un apprentissage automatique. Depuis ces trois dernières décennies, les RNAs ont connu de nombreuses avancées majeures. Notamment avec les réseaux de convolution ou les mécanismes d'attention. Ces avancées ont permis le développement de la reconnaissance d'images, des modèles de langage géants ou de la conversion texte-image.En 1943, les travaux de McCulloch et Pitt sur le neurone formel faciliteront la naissance des premiers RNAs appelés perceptrons, et décrits pour la

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10

Lévesque, Julien-Charles. "Bayesian hyperparameter optimization : overfitting, ensembles and conditional spaces." Doctoral thesis, Université Laval, 2018. http://hdl.handle.net/20.500.11794/28364.

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Dans cette thèse, l’optimisation bayésienne sera analysée et étendue pour divers problèmes reliés à l’apprentissage supervisé. Les contributions de la thèse sont en lien avec 1) la surestimation de la performance de généralisation des hyperparamètres et des modèles résultants d’une optimisation bayésienne, 2) une application de l’optimisation bayésienne pour la génération d’ensembles de classifieurs, et 3) l’optimisation d’espaces avec une structure conditionnelle telle que trouvée dans les problèmes “d’apprentissage machine automatique” (AutoML). Généralement, les algorithmes d’apprentissage

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Nygren, Rasmus. "Evaluation of hyperparameter optimization methods for Random Forest classifiers." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-301739.

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In order to create a machine learning model, one is often tasked with selecting certain hyperparameters which configure the behavior of the model. The performance of the model can vary greatly depending on how these hyperparameters are selected, thus making it relevant to investigate the effects of hyperparameter optimization on the classification accuracy of a machine learning model. In this study, we train and evaluate a Random Forest classifier whose hyperparameters are set to default values and compare its classification accuracy to another classifier whose hyperparameters are obtained thr

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12

Matosevic, Antonio. "On Bayesian optimization and its application to hyperparameter tuning." Thesis, Linnéuniversitetet, Institutionen för matematik (MA), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-74962.

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This thesis introduces the concept of Bayesian optimization, primarly used in optimizing costly black-box functions. Besides theoretical treatment of the topic, the focus of the thesis is on two numerical experiments. Firstly, different types of acquisition functions, which are the key components responsible for the performance, are tested and compared. Special emphasis is on the analysis of a so-called exploration-exploitation trade-off. Secondly, one of the most recent applications of Bayesian optimization concerns hyperparameter tuning in machine learning algorithms, where the objective fun

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Schilling, Nicolas [Verfasser], Lars [Akademischer Betreuer] Schmidt-Thieme, and Frank [Gutachter] Hutter. "Bayesian Hyperparameter Optimization - Relational and Scalable Surrogate Models for Hyperparameter Optimization Across Problem Instances / Nicolas Schilling ; Gutachter: Frank Hutter ; Betreuer: Lars Schmidt-Thieme." Hildesheim : Stiftung Universität Hildesheim, 2019. http://d-nb.info/1199005703/34.

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14

Larsson, Olov. "A Reward-based Algorithm for Hyperparameter Optimization of Neural Networks." Thesis, Karlstads universitet, Institutionen för matematik och datavetenskap (from 2013), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kau:diva-78827.

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Machine learning and its wide range of applications is becoming increasingly prevalent in both academia and industry. This thesis will focus on the two machine learning methods convolutional neural networks and reinforcement learning. Convolutional neural networks has seen great success in various applications for both classification and regression problems in a diverse range of fields, e.g. vision for self-driving cars or facial recognition. These networks are built on a set of trainable weights optimized on data, and a set of hyperparameters set by the designer of the network which will rema

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Jeggle, Kai. "Scalable Hyperparameter Opimization: Combining Asynchronous Bayesian Optimization With Efficient Budget Allocation." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-280340.

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Automated hyperparameter tuning has become an integral part in the optimization of machine learning (ML) pipelines. Sequential model based optimization algorithms, such as bayesian optimization (BO), have been proven to be sample efficient with strong final performance. However, the increasing complexity and training times of ML models requires a shift from sequential to asynchronous, distributed hyperparameter tuning. The literature has come up with different strategies to modify BO to work in an asynchronous setting. By combining asynchronous BO with budget allocation strategies, poor perfor

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Gabere, Musa Nur. "Prediction of antimicrobial peptides using hyperparameter optimized support vector machines." Thesis, University of the Western Cape, 2011. http://etd.uwc.ac.za/index.php?module=etd&action=viewtitle&id=gen8Srv25Nme4_7345_1330684697.

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<p>Antimicrobial peptides (AMPs) play a key role in the innate immune response. They can be ubiquitously found in a wide range of eukaryotes including mammals, amphibians, insects, plants, and protozoa. In lower organisms, AMPs function merely as antibiotics by permeabilizing cell membranes and lysing invading microbes. Prediction of antimicrobial peptides is important because experimental methods used in characterizing AMPs are costly, time consuming and resource intensive and identification of AMPs in insects can serve as a template for the design of novel antibiotic. In order to fulfil this

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Hauser, Kristen. "Hyperparameter Tuning for Reinforcement Learning with Bandits and Off-Policy Sampling." Case Western Reserve University School of Graduate Studies / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=case1613034993418088.

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Denton, Trip Shokoufandeh Ali. "Subset selection using nonlinear optimization /." Philadelphia, Pa. : Drexel University, 2007. http://hdl.handle.net/1860/1763.

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Clune, Rory P. (Rory Patrick). "Algorithm selection in structural optimization." Thesis, Massachusetts Institute of Technology, 2013. http://hdl.handle.net/1721.1/82832.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Department of Civil and Environmental Engineering, 2013.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 153-162).<br>Structural optimization is largely unused as a practical design tool, despite an extensive academic literature which demonstrates its potential to dramatically improve design processes and outcomes. Many factors inhibit optimization's application. Among them is the requirement for engineers-who generally lack the requisite expertise-to choose an optimization algorithm for a given prob

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20

Silvestre, Fialho Álvaro Roberto. "Adaptive operator selection for optimization." Paris 11, 2010. http://www.theses.fr/2010PA112292.

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Les Algorithmes Evolutionnaires sont des algorithmes d'optimisation qui ont déjà montré leur efficacité dans plusieurs domaines ; mais leur performance dépend du réglage de plusieurs paramètres. Cette thèse est consacrée au développement de techniques pour automatiser ce réglage par le biais de l'apprentissage automatique. Plus spécifiquement, nous avons travaillé sur un sous-problème : étant donné un ensemble d'opérateurs, cela consiste à choisir lequel doit être appliqué pour la génération de chaque nouvelle solution, basé sur la performance connue de chaque opérateur. Cette approche est uti

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Krüger, Franz David, and Mohamad Nabeel. "Hyperparameter Tuning Using Genetic Algorithms : A study of genetic algorithms impact and performance for optimization of ML algorithms." Thesis, Mittuniversitetet, Institutionen för informationssystem och –teknologi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-42404.

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Maskininlärning har blivit allt vanligare inom näringslivet. Informationsinsamling med Data mining (DM) har expanderats och DM-utövare använder en mängd tumregler för att effektivisera tillvägagångssättet genom att undvika en anständig tid att ställa in hyperparametrarna för en given ML-algoritm för nå bästa träffsäkerhet. Förslaget i denna rapport är att införa ett tillvägagångssätt som systematiskt optimerar ML-algoritmerna med hjälp av genetiska algoritmer (GA), utvärderar om och hur modellen ska konstrueras för att hitta globala lösningar för en specifik datamängd. Genom att implementera g

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Bardenet, Rémi. "Towards adaptive learning and inference : applications to hyperparameter tuning and astroparticle physics." Thesis, Paris 11, 2012. http://www.theses.fr/2012PA112307.

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Les algorithmes d'inférence ou d'optimisation possèdent généralement des hyperparamètres qu'il est nécessaire d'ajuster. Nous nous intéressons ici à l'automatisation de cette étape d'ajustement et considérons différentes méthodes qui y parviennent en apprenant en ligne la structure du problème considéré.La première moitié de cette thèse explore l'ajustement des hyperparamètres en apprentissage artificiel. Après avoir présenté et amélioré le cadre générique de l'optimisation séquentielle à base de modèles (SMBO), nous montrons que SMBO s'applique avec succès à l'ajustement des hyperparamètres d

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Carlson, Susan Elizabeth. "Component selection optimization using genetic algorithms." Diss., Georgia Institute of Technology, 1993. http://hdl.handle.net/1853/17886.

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Wu, Joseph T. (Joseph Tszkei) 1977. "Optimization of influenza vaccine strain selection." Thesis, Massachusetts Institute of Technology, 2003. http://hdl.handle.net/1721.1/29600.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2003.<br>Includes bibliographical references (p. 89-90).<br>The World Health Organization (WHO) is responsible for making annual vaccine strains recommendation to countries around the globe. However, various studies have found that the WHO vaccine selection strategy has not been effective in some years. This motivates the search for a better strategy for choosing vaccine strains. In this work, we use recent results from theoretical immunology to formulate the vaccine selection proble

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Puhle, Michael. "Bond portfolio optimization." Berlin Heidelberg Springer, 2007. http://d-nb.info/985928115/04.

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Hugo, AndreÌ. "Environmentally conscious process selection, design and optimization." Thesis, Imperial College London, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.417505.

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Huang, Yu'e. "An optimization of feature selection for classification." Thesis, University of Ulster, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.428284.

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Persson, Mikael. "Cableharness selection for gearboxes using mathematical optimization." Thesis, KTH, Optimeringslära och systemteori, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-209929.

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The Scania modular product system enables the production of thousands of different versions of gearboxes. If each version use a unique cable harness, this leads to large costs for storage and production. It is desired to find a smaller set of cable harnesses to fit the needs of all gearboxes. In this report we present two mathematical programming models to accomplish this while minimizing cost for production and storage. We propose a procedure for partitioning the data into smaller subsets without loosing model accuracy. We also show how the solution to the first model may be used as a warm st

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Klasila, A. (Aleksi). "Mbed OS regression test selection and optimization." Master's thesis, University of Oulu, 2019. http://jultika.oulu.fi/Record/nbnfioulu-201908312830.

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Abstract. Testing is a fundamental building block in the identification of bugs, errors and defects in both hardware and software. Effective testing of large projects requires automated testing, test selection and test optimization. Using CI (Continuous Integration) tools, and test selection and optimization techniques reduce development time and increase productivity. The prioritization, selection and minimization of tests are well-known problems in software testing. Arm Mbed OS is a free, open-source embedded operating system designed specifically for the “things” in the IoT (Internet of

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Shende, Sourabh. "Bayesian Topology Optimization for Efficient Design of Origami Folding Structures." University of Cincinnati / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1592170569337763.

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31

Müller, Stephan. "Constrained portfolio optimization /." [S.l.] : [s.n.], 2005. http://aleph.unisg.ch/hsgscan/hm00133325.pdf.

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SCHLITTLER, JOAO GABRIEL FELIZARDO S. "PORTFOLIO SELECTION VIA DATA-DRIVEN DISTRIBUTIONALLY ROBUST OPTIMIZATION." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2018. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=36002@1.

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PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO<br>COORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR<br>PROGRAMA DE SUPORTE À PÓS-GRADUAÇÃO DE INSTS. DE ENSINO<br>PROGRAMA DE EXCELENCIA ACADEMICA<br>Otimização de portfólio tradicionalmente assume ter conhecimento da distribuição de probabilidade dos retornos ou pelo menos algum dos seus momentos. No entanto, é sabido que a distribuição de probabilidade dos retornos muda com frequência ao longo do tempo, tornando difícil a utilização prática de modelos puramente estatísticos, que confiam indubitavelmente em uma distribuição estima

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Yu, Baosheng. "Robust Diversity-Driven Subset Selection in Combinatorial Optimization." Thesis, The University of Sydney, 2019. http://hdl.handle.net/2123/19834.

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Subset selection is fundamental in combinatorial optimization with applications in biology, operations research, and computer science, especially machine learning and computer vision. However, subset selection has turned out to be NP-hard and polynomial-time solutions are usually not available. Therefore, it is of great importance to develop approximate algorithms with theoretical guarantee for subset selection in constrained settings. To select a diverse subset with an asymmetric objective function, we develop an asymmetric subset selection method, which is computationally efficient and has

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Issa, Tina. "Multiobjective optimization and feature selection in deep learning." Electronic Thesis or Diss., université Paris-Saclay, 2024. http://www.theses.fr/2024UPASG056.

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Les avancées technologiques ont considérablement impacté l'analyse des données, en particulier avec l'essor du Big Data. L'apprentissage profond a émergé comme une solution puissante pour gérer la complexité et le volume des données. Les modèles profonds utilisent plusieurs niveaux d'abstraction pour extraire des motifs complexes. Leur efficacité a été démontrée dans diverses tâches, notamment la reconnaissance d'images.Cependant en génomique, les nouvelles techniques de séquençage produisent des quantités massives de données, où le nombre de variables dépasse largement le nombred'échantillons

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Toy, Ayhan Özgür. "Route, aircraft prioritization and selection for airlift mobility optimization /." Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 1996. http://handle.dtic.mil/100.2/ADA326731.

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Thesis (M.S. in Operations Research) Naval Postgraduate School, September 1996.<br>"September 1996." Thesis advisors, Richard E. Rosenthal, Steven F. Baker. Includes bibliographical references (p. 67). Also available online.

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Toy, Ayhan Özgür. "Route, aircraft prioritization and selection for airlift mobility optimization." Thesis, Monterey, California. Naval Postgraduate School, 1996. http://hdl.handle.net/10945/8932.

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Approved for public release; distribution in unlimited.<br>The Throughput II mobility optimization model was developed at the Naval Postgraduate School for the Air Force Studies and Analysis Agency (AFSAA). The purpose of Throughput II is to help answer questions about the ability of the USAF to conduct airlift of soldiers and equipment in support of major military operations. Repeated runs of this model have helped AFSAA generate insights and recommendations concerning the selection of aircraft assets. Although Throughput II has earned the confidence of AFSAA, repeated applications are hamper

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Zhernova, P. "Optimization methods for the selection of protective printing complex." Thesis, НТМТ, 2015. http://openarchive.nure.ua/handle/document/8357.

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Hodges, Clayton Christopher. "Optimization of BMP Selection for Distributed Stormwater Treatment Networks." Diss., Virginia Tech, 2016. http://hdl.handle.net/10919/81698.

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Current site scale stormwater management designs typically include multiple distributed stormwater best management practices (BMPs), necessary to meet regulatory objectives for nutrient removal and groundwater recharge. Selection of the appropriate BMPs for a particular site requires consideration of contributing drainage area characteristics, such as soil type, area, and land cover. Other physical constraints such as karst topography, areas of highly concentrated pollutant runoff, etc. as well as economics, such as installation and operation and maintenance cost must be considered. Due to

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Olsson, Sam. "Optimization model for selection of switches at railway stations." Thesis, Linköpings universitet, Tillämpad matematik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-177613.

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The goal of this project is to implement and verify an optimization model for finding a min-cost selection of switches and train paths at railway stations. The selected train paths must satisfy traffic requirements that commonly apply to regular railway traffic. The requirements include different combinations of simultaneous and overtaking train movements. The model does not rely on timetables but does instead utilize different path sets that are produced via algorithms based on a network representation of the station layout. The model has been verified on a small test station and also on the

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Ujihara, Rintaro. "Multi-objective optimization for model selection in music classification." Thesis, KTH, Optimeringslära och systemteori, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-298370.

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With the breakthrough of machine learning techniques, the research concerning music emotion classification has been getting notable progress combining various audio features and state-of-the-art machine learning models. Still, it is known that the way to preprocess music samples and to choose which machine classification algorithm to use depends on data sets and the objective of each project work. The collaborating company of this thesis, Ichigoichie AB, is currently developing a system to categorize music data into positive/negative classes. To enhance the accuracy of the existing system, thi

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Baumgarten, Peter B. "Optimization of United States Marine Corps Officer Career Path Selection." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2000. http://handle.dtic.mil/100.2/ADA381837.

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Thesis (M.S. in Operations Research) Naval Postgraduate School, September 2000.<br>Thesis advisor, Siriphong Lawphongpanich. "September 200." Includes bibliographical references (p. 67). Also available online.

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Lourens, Mark. "Integer optimization for the selection of a twenty20 cricket team." Thesis, Nelson Mandela Metropolitan University, 2008. http://hdl.handle.net/10948/1000.

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During the last few years, much effort has been devoted to measuring the ability of sport teams, as well as that of the individual players. Much research has been on the game of cricket, and the comparison, or ranking, of players according to their abilities. This study continues preceding research using an optimization approach, namely, a binary integer programme, to select an SA domestic Pro20 cricket team.

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Fraleigh, Lisa Marie. "Optimal sensor selection and parameter estimation for real-time optimization." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp01/MQ40050.pdf.

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Verdugo, Silva Víctor Ignacio. "Convex and online optimization: Applications to scheduling and selection problems." Tesis, Universidad de Chile, 2018. http://repositorio.uchile.cl/handle/2250/168128.

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Doctor en Sistemas de Ingeniería en cotutela con Ecole Normale Supérieure<br>Convex optimization has been a powerful tool for designing algorithms. In practice is a widely used in areas such as operations research and machine learning, but also in many fundamental combinatorial problems they yield to the best know approximations algorithms providing unconditional guarantees over the solution quality. In the first part of this work we study the effect of constructing convex relaxations to a packing problem, based on applying lift & project methods. We exhibit a weakness of this relaxations whe

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Meoni, Francesco <1987&gt. "Modeling, Component Selection and Optimization of Servo-controlled Automatic Machinery." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2017. http://amsdottorato.unibo.it/8140/1/Meoni_Francesco_tesi.pdf.

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A servo-controlled automatic machine can perform tasks that involve synchronized actuation of a significant number of servo-axes, namely one degree-of-freedom (DoF) electromechanical actuators. Each servo-axis comprises a servo-motor, a mechanical transmission and an end-effector, and is responsible for generating the desired motion profile and providing the power required to achieve the overall task. The design of a such a machine must involve a detailed study from a mechatronic viewpoint, due to its electric and mechanical nature. The first objective of this thesis is the development of a

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Sforza, Eleonora. "Oil from microalgae: species selection, photobioreactor design and process optimization." Doctoral thesis, Università degli studi di Padova, 2012. http://hdl.handle.net/11577/3421970.

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This PhD research project was focused on microalgal oil production for biofuel. The work includes mostly an experimental part on microalgal species selection and optimization of growth conditions and a part of process simulation and photobioreactor design. After an overview of literature on the algal biology and cultivation and photobioreactor design, the experimental activities started with the set up of materials, methods and experimental apparatus, several microalgal species were screened, in order to select the most promising ones from an industrial point of view. In addition, an experim

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Ayoub, Issa. "Multimodal Affective Computing Using Temporal Convolutional Neural Network and Deep Convolutional Neural Networks." Thesis, Université d'Ottawa / University of Ottawa, 2019. http://hdl.handle.net/10393/39337.

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Affective computing has gained significant attention from researchers in the last decade due to the wide variety of applications that can benefit from this technology. Often, researchers describe affect using emotional dimensions such as arousal and valence. Valence refers to the spectrum of negative to positive emotions while arousal determines the level of excitement. Describing emotions through continuous dimensions (e.g. valence and arousal) allows us to encode subtle and complex affects as opposed to discrete emotions, such as the basic six emotions: happy, anger, fear, disgust, sad and n

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Steele, Steven Cory Wyatt. "Optimal Engine Selection and Trajectory Optimization using Genetic Algorithms for Conceptual Design Optimization of Resuable Launch Vehicles." Thesis, Virginia Tech, 2015. http://hdl.handle.net/10919/51771.

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Proper engine selection for Reusable Launch Vehicles (RLVs) is a key factor in the design of low cost reusable launch systems for routine access to space. RLVs typically use combinations of different types of engines used in sequence over the duration of the flight. Also, in order to properly choose which engines are best for an RLV design concept and mission, the optimal trajectory that maximizes or minimizes the mission objective must be found for that engine configuration. Typically this is done by the designer iteratively choosing engine combinations based on his/her judgment and running e

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Zhao, Feng 1992. "Advanced pixel selection and optimization algorithms for Persistent Scatterer Interferometry (PSI)." Doctoral thesis, Universitat Politècnica de Catalunya, 2019. http://hdl.handle.net/10803/668472.

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Ground deformation measurements can provide valuable information for minimization of associated loss and damage caused by natural and environmental hazards. As a kind of remote sensing technique, Persistent Scatterer Interferometry (PSI) SAR is able to measure ground deformation with high spatial resolution, efficiently. Moreover, the ground deformation monitoring accuracy of PSI techniques can reach up to millimeter level. However, low coherence could hinderthe exploitation of SAR data, and high-accuracy deformation monitoring can only be achieved by PSI for high quality pixels. Therefore, p

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Ruan, Tieming. "Selection and optimization of snap-fit features via web-based software." Columbus, Ohio : Ohio State University, 2005. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1133282089.

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