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Dissertations / Theses on the topic 'Boosting'

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Published: 4 June 2021
Last updated: 25 July 2025

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1

Lin, Wei-Chao. "Boosting image annotation." Thesis, University of Sunderland, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.512013.

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2

Thompson, Simon Giles. "Distributed boosting algorithms." Thesis, University of Portsmouth, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.285529.

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Zhou, Mian. "Gobor-boosting face recognition." Thesis, University of Reading, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.494814.

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In the past decade, automatic face recognition has received much attention by both the commercial and public sectors as an efficient and resilient recognition technique in biometrics. This thesis describes a highly accurate appearance-based algorithm for grey scale front-view face recognition - Gabor-Boosting face recognition by means of computer vision, pattern recognition, image processing, machine learning etc. The strong performance of the Gabor-boosting face recognition algorithm is highlighted by combining three key leading edge techniques - the Gabor wavelet transform, AdaBoost, Support
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4

ANIBOLETE, TULIO JORGE DE A. N. DE S. "BOOSTING FOR RECOMMENDATION SYSTEMS." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2008. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=13225@1.

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Com a quantidade de informação e sua disponibilidade facilitada pelo uso da Internet, diversas opções são oferecidas às pessoas e estas, normalmente, possuem pouca ou quase nenhuma experiência para decidir dentre as alternativas existentes. Neste âmbito, os Sistemas de Recomendação surgem para organizar e recomendar automaticamente, através de Aprendizado de Máquina, itens interessantes aos usuários. Um dos grandes desafios deste tipo de sistema é realizar o casamento correto entre o que está sendo recomendado e aqueles que estão recebendo a recomendação. Este trabalho aborda um Sistema de Rec
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SALOMONI, MATTEO. "Boosting scintillation based detection." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2019. http://hdl.handle.net/10281/241285.

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Durante il mio dottorato di ricerca ho studiato in modo approfondito I cristalli scintillanti, trovando diversi limiti legati all’emissione di luce, proprietà ottiche e stabilità chimica. Sono stati sviluppati diversi banchi di lavoro specifici per le caratterizzazioni presentate nella tesi e molto lavoro è stato dedicato alla finalizzazione dei programmi di simulazione necessari alla descrizione del sistema scintillatore-photorivelatore. Uno studio della maggior parte degli Approcci classici, sul tema dell’ottimizzazione degli scintillatori, ha portato a confermare come si sia arrivati ad un
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Hofner, Benjamin. "Boosting in structured additive models." Diss., lmu, 2011. http://nbn-resolving.de/urn:nbn:de:bvb:19-138053.

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7

Rätsch, Gunnar. "Robust boosting via convex optimization." Phd thesis, Universität Potsdam, 2001. http://opus.kobv.de/ubp/volltexte/2005/39/.

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In dieser Arbeit werden statistische Lernprobleme betrachtet. Lernmaschinen extrahieren Informationen aus einer gegebenen Menge von Trainingsmustern, so daß sie in der Lage sind, Eigenschaften von bisher ungesehenen Mustern - z.B. eine Klassenzugehörigkeit - vorherzusagen. Wir betrachten den Fall, bei dem die resultierende Klassifikations- oder Regressionsregel aus einfachen Regeln - den Basishypothesen - zusammengesetzt ist. Die sogenannten Boosting Algorithmen erzeugen iterativ eine gewichtete Summe von Basishypothesen, die gut auf ungesehenen Mustern vorhersagen. <br /> Die Arbeit behandelt
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8

Chan, Jeffrey (Jeffrey D. ). "On boosting and noisy labels." Thesis, Massachusetts Institute of Technology, 2015. http://hdl.handle.net/1721.1/100297.

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Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2015.<br>This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Cataloged from student-submitted PDF version of thesis.<br>Includes bibliographical references (pages 53-56).<br>Boosting is a machine learning technique widely used across many disciplines. Boosting enables one to learn from labeled data in order to predict the labels of unlabeled data. A central property of boosting instru
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9

Bjurgert, Johan. "System Identification by Adaptive Boosting." Thesis, KTH, Reglerteknik, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-179711.

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In the field of machine learning, the algorithm Adaptive Boosting has beensuccessfully applied to a wide range of regression and classification problems.Still, there is no known method to use the algorithm to estimate dynamical systems.In this thesis, the relationship between Adaptive Boosting and systemidentification is explored. A new identification method, inspired by AdaptiveBoosting, called TM-Boost is introduced. It fits a dynamical model byiteratively adding orthonormal basis functions. An interesting feature of themethod is that there is no need to specify a model order. It is also pro
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10

Mayr, Andreas [Verfasser]. "Boosting beyond the mean - extending component-wise gradient boosting algorithms to multiple dimensions / Andreas Mayr." München : Verlag Dr. Hut, 2013. http://d-nb.info/104287848X/34.

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11

Reithinger, Florian. "Mixed models based on likelihood boosting." Diss., [S.l.] : [s.n.], 2006. http://edoc.ub.uni-muenchen.de/archive/00006281.

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12

Tieu, Kinh H. (Kinh Han) 1976. "Boosting sparse representations for image retrieval." Thesis, Massachusetts Institute of Technology, 2000. http://hdl.handle.net/1721.1/86431.

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13

TEIXEIRA, JÚNIOR Talisman Cláudio de Queiroz. "Classificação fonética utilizando Boosting e SVM." Universidade Federal do Pará, 2006. http://repositorio.ufpa.br/jspui/2011/2533.

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Submitted by Irvana Coutinho (irvana@ufpa.br) on 2012-03-07T12:35:04Z No. of bitstreams: 2 Dissertacao_Talisman_Teixeira_Junior ClassificacaoFoneticaBoosting.pdf: 1955727 bytes, checksum: 2174e57105a6d0135a85cb9c47e05a7a (MD5) license_rdf: 23898 bytes, checksum: e363e809996cf46ada20da1accfcd9c7 (MD5)<br>Approved for entry into archive by Irvana Coutinho(irvana@ufpa.br) on 2012-03-07T12:40:11Z (GMT) No. of bitstreams: 2 Dissertacao_Talisman_Teixeira_Junior ClassificacaoFoneticaBoosting.pdf: 1955727 bytes, checksum: 2174e57105a6d0135a85cb9c47e05a7a (MD5) license_rdf: 23898 bytes, checksum:
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14

Nikolaou, Nikolaos. "Cost-sensitive boosting : a unified approach." Thesis, University of Manchester, 2016. https://www.research.manchester.ac.uk/portal/en/theses/costsensitive-boosting-a-unified-approach(ae9bb7bd-743e-40b8-b50f-eb59461d9d36).html.

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In this thesis we provide a unifying framework for two decades of work in an area of Machine Learning known as cost-sensitive Boosting algorithms. This area is concerned with the fact that most real-world prediction problems are asymmetric, in the sense that different types of errors incur different costs. Adaptive Boosting (AdaBoost) is one of the most well-studied and utilised algorithms in the field of Machine Learning, with a rich theoretical depth as well as practical uptake across numerous industries. However, its inability to handle asymmetric tasks has been the subject of much criticis
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15

Zhai, Shaodan. "Direct Optimization for Classification with Boosting." Wright State University / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=wright1453001665.

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16

Byrne, Alice. "Boosting Britain : démocratie et propagande culturelle." Aix-Marseille 1, 2010. http://www.theses.fr/2010AIX10026.

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Cette étude porte sur une revue du British Council : Britain To-day, (1939-1954). Nous définissons Britain To-day comme un vecteur de "propagande culturelle" dont le but était de promouvoir une image positive de la Grande-Bretagne à l'étranger. Il s'agit de comprendre comment le British Council cherchait à atteindre cet objectif sur une durée relativement longue. Ce travail suppose une analyse des différents thèmes développées par British To-day, en soulignant leur rapport avec la politique extérieure britannique. La première et deuxième parties couvrent respectivement la période de l'avant-gu
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17

Selchenkova, Tatiana. "Boosting implicit learning with temporal regularities." Thesis, Lyon 1, 2013. http://www.theses.fr/2013LYO10278.

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L'apprentissage implicite est une acquisition d'information complexe sans intention d'apprendre. Le but de cette thèse est de déterminer comment des régularités temporelles peuvent influencer l'apprentissage implicite d'une grammaire artificielle basée sur des structures de hauteur des notes. Selon la théorie de l'attention dynamique (Jones, 1976), il y a une synchronisation entre des régularités temporelles des événements externes et des oscillateurs internes qui guide l'attention à travers le temps et aide à développer les attentes perceptives et temporelles. Notre hypothèse est que des stru
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18

Suchier, Henri-Maxime. "Nouvelles contributions du boosting en apprentissage automatique." Phd thesis, Université Jean Monnet - Saint-Etienne, 2006. http://tel.archives-ouvertes.fr/tel-00379539.

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L'apprentissage automatique vise la production d'une hypothèse modélisant un concept à partir d'exemples, dans le but notamment de prédire si de nouvelles observations relèvent ou non de ce concept. Parmi les algorithmes d'apprentissage, les méthodes ensemblistes combinent des hypothèses de base (dites ``faibles'') en une hypothèse globale plus performante.<br /><br />Le boosting, et son algorithme AdaBoost, est une méthode ensembliste très étudiée depuis plusieurs années : ses performances expérimentales remarquables reposent sur des fondements théoriques rigoureux. Il construit de manière ad
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19

Vayatis, Nicolas. "Approches statistiques en apprentissage : boosting et ranking." Habilitation à diriger des recherches, Université Pierre et Marie Curie - Paris VI, 2006. http://tel.archives-ouvertes.fr/tel-00120738.

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Depuis une dizaine d'années, la théorie statistique de l'apprentissage a connu une forte expansion. L'avènement d'algorithmes hautement performants pour la classification de données en grande dimension, tels que le boosting ou les machines à noyaux (SVM) a engendré de nombreuses questions statistiques que la théorie de Vapnik-Chervonenkis (VC) ne permettait pas de résoudre. En effet, le principe de Minimisation du Risque Empirique ne rend pas compte des méthodes d'apprentissage concrètes et le concept de complexité combinatoire de VC dimension ne permet pas d'expliquer les capacités de général
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20

Hofner, Benjamin [Verfasser]. "Boosting in Structured Additive Models / Benjamin Hofner." München : Verlag Dr. Hut, 2012. http://d-nb.info/1020299223/34.

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21

Wang, Shihai. "Boosting learning applied to facial expression recognition." Thesis, University of Manchester, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.511940.

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22

Guile, Geofrrey Robert. "Boosting ensemble techniques for Microarray data analysis." Thesis, University of East Anglia, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.518361.

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23

Necib, Lina. "Boosting (in)direct detection of dark matter." Thesis, Massachusetts Institute of Technology, 2017. http://hdl.handle.net/1721.1/112073.

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Thesis: Ph. D., Massachusetts Institute of Technology, Department of Physics, 2017.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 153-178).<br>In this thesis, I study the expected direct and indirect detection signals of dark matter. More precisely, I study three aspects of dark matter; I use hydrodynamic simulations to extract properties of weakly interacting dark matter that are relevant for both direct and indirect detection signals, and construct viable dark matter models with interesting experimental signatures. First, I analyze the full scale Illu
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24

Iyer, Raj Dharmarajan 1976. "An efficient boosting algorithm for combining preferences." Thesis, Massachusetts Institute of Technology, 1999. http://hdl.handle.net/1721.1/80203.

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Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1999.<br>Includes bibliographical references (p. 79-84).<br>by Raj Dharmarajan Iyer, Jr.<br>S.M.
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25

Henry, Claudia. "Approches spectrales et boosting : extensions et synergie." Université des Antilles et de la Guyane, 2008. http://www.theses.fr/2008AGUY0217.

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L'apprentissage automatique est un champ d'investigation important en intelligence artificielle. Nous l'envisageons sous ses deux aspects : supervisé et non-supervisé, respectivement via deux techniques : Ie clustering spectral et Ie boosting. Le clustering spectral qui est basé sur des résultats algébriques s'est avéré être simple a utiliser et efficace. Nous proposons une interprétation probabiliste de cette méthode et une application à la distinction de langues dans un corpus de textes multilingues. Le boosting est une technique d'apprentissage permettant d'augmenter les performances d'arbr
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26

Larsson, Richard. "Boosting Gamma Neural Activity using Binaural Beats." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-166074.

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In this paper, binaural beats were used as stimuli to induce Gamma neural activity in the brains of 18 participants with the purpose to see if the effect enhanced memory and/or speech perception. Participants conducted a word-list recall task, followed by a speech-in-noise task under three conditions: before Gamma stimulus, after Gamma stimulus, and after a placebo stimulus. The results showed that the method works to boost Gamma neural activity, but that neither memory nor speech-perception was significantly affected by the stimulus. The conclusion is that binaural beats is unreliable as a me
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27

Teixeira, Filipe. "Boosting compression-based classifiers for authorship attribution." Master's thesis, Universidade de Aveiro, 2016. http://hdl.handle.net/10773/18375.

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Mestrado em Engenharia de Computadores e Telemática<br>Atribuição de autoria é o ato de atribuir um autor a documento anónimo. Apesar de esta tarefa ser tradicionalmente feita por especialistas, muitos novos métodos foram apresentados desde o aparecimento de computadores, em meados do século XX, alguns deles recorrendo a compressores para encontrar padrões recorrentes nos dados. Neste trabalho vamos apresentar os resultados que podem ser alcançados ao utilizar mais do que um compressor, utilizando um meta-algoritmo conhecido como Boosting.<br>Authorship attribution is the task of assign
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Huang, Jian Giles C. Lee. "A multiclass boosting classification method with active learning." [University Park, Pa.] : Pennsylvania State University, 2009. http://etda.libraries.psu.edu/theses/approved/WorldWideIndex/ETD-4765/index.html.

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Rätsch, Gunnar. "Robust boosting via convex optimization theory and applications /." [S.l.] : [s.n.], 2001. http://pub.ub.uni-potsdam.de/2002/0008/raetsch.ps.

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Lei, Celestino. "Using genetic algorithms and boosting for data preprocessing." Thesis, University of Macau, 2002. http://umaclib3.umac.mo/record=b1447848.

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Mitchell, Andrew Computer Science &amp Engineering Faculty of Engineering UNSW. "An approach to boosting from positive-only data." Awarded by:University of New South Wales. Computer Science and Engineering, 2004. http://handle.unsw.edu.au/1959.4/20678.

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Ensemble techniques have recently been used to enhance the performance of machine learning methods. However, current ensemble techniques for classification require both positive and negative data to produce a result that is both meaningful and useful. Negative data is, however, sometimes difficult, expensive or impossible to access. In this thesis a learning framework is described that has a very close relationship to boosting. Within this framework a method is described which bears remarkable similarities to boosting stumps and that does not rely on negative examples. This is surprising since
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32

Liao, Jun. "Totally corrective boosting algorithms that maximize the margin /." Diss., Digital Dissertations Database. Restricted to UC campuses, 2006. http://uclibs.org/PID/11984.

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33

Robinzonov, Nikolay. "Advances in boosting of temporal and spatial models." Diss., Ludwig-Maximilians-Universität München, 2013. http://nbn-resolving.de/urn:nbn:de:bvb:19-153382.

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Boosting is an iterative algorithm for functional approximation and numerical optimization which can be applied to solve statistical regression-type problems. By design, boosting can mimic the solutions of many conventional statistical models, such as the linear model, the generalized linear model, and the generalized additive model, but its strength is to enhance these models or even go beyond. It enjoys increasing attention since a) it is a generic algorithm, easily extensible to exciting new problems, and b) it can cope with``difficult'' data where conventional statistical models fail. In
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34

DUARTE, JULIO CESAR. "THE BOOSTING AT START ALGORITHM AND ITS APPLICATIONS." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2009. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=31451@1.

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PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO<br>COORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR<br>INSTITUTO MILITAR DE ENGENHARIA<br>CENTRO TECNOLÓGICO DO EXÉRCITO<br>PROGRAMA DE EXCELENCIA ACADEMICA<br>Boosting é uma técnica de aprendizado de máquina que combina diversos classificadores fracos com o objetivo de melhorar a acurácia geral. Em cada iteração, o algoritmo atualiza os pesos dos exemplos e constrói um classificador adicional. Um esquema simples de votação é utilizado para combinar os classificadores. O algoritmo mais famoso baseado em Boosting é o AdaBoost. Este
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Abouelenien, Mohamed. "Boosting for Learning From Imbalanced, Multiclass Data Sets." Thesis, University of North Texas, 2013. https://digital.library.unt.edu/ark:/67531/metadc407775/.

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In many real-world applications, it is common to have uneven number of examples among multiple classes. The data imbalance, however, usually complicates the learning process, especially for the minority classes, and results in deteriorated performance. Boosting methods were proposed to handle the imbalance problem. These methods need elongated training time and require diversity among the classifiers of the ensemble to achieve improved performance. Additionally, extending the boosting method to handle multi-class data sets is not straightforward. Examples of applications that suffer from imbal
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Barbosa, Paulo Henrique Farias. "The impact of boosting higher education in Brazil." Master's thesis, Instituto Superior de Economia e Gestão, 2018. http://hdl.handle.net/10400.5/16614.

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Mestrado em Econometria Aplicada e Previsão<br>A presente tese estuda se a abertura de Instituição de Ensino Superior (IES) em municípios do Brasil onde ainda não havia oferta de ensino superior, teve impacto na renda per capita. Para isso, construí um painel com todos os municípios brasileiros para os anos de 2000 e 2010, com os dados do Censo Demográfico e Censo do Ensino Superior. Para lidar com possíveis problemas de endogeneidade que são comuns neste tipo de dados, usei o modelo de Heckman. Os resultados do modelo de Heckman apontam para um positivo impacto da abertura de IES na renda per
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Sogoni, Zanele. "Is public debt boosting economic growth in SADC?" Master's thesis, University of Cape Town, 2014. http://hdl.handle.net/11427/29033.

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The World Bank estimates that Africa's inadequate infrastructure decreases productivity by around 40 per cent every year and reduces national economic growth by 2 per cent annually. Such disadvantages hinder private sector investment, which is a key driver of job and wealth creation. Financing the development of infrastructure in an appropriate manner has been a leading topic in the continents development agenda. In order to remedy the infrastructure deficit problem, more and more African countries are increasing their public debts by borrowing in the international markets to finance their inf
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Dhyani, Dushyanta Dhyani. "Boosting Supervised Neural Relation Extraction with Distant Supervision." The Ohio State University, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=osu1524095334803486.

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Burridge, Stephen (Stephen Robert) Carleton University Dissertation History. "The busy East: boosting the Maritimes, 1910-1925." Ottawa, 1993.

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Loh, Wai Lam. "Boosting of multiphase flows using multiphase jet pumps." Thesis, University of Manchester, 2000. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.549306.

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Moreni, Matilde <1994&gt. "Prediction of Cryptocurrency prices using Gradient Boosting machine." Master's Degree Thesis, Università Ca' Foscari Venezia, 2020. http://hdl.handle.net/10579/17739.

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The Gradient Boosting is a machine learning approach that is widely used due to its high performance and accuracy. The aim of this thesis is find out how good is the performance of Gradient Boosting applied to the price forecasting of Cryptocurrencies and then to flat currencies. The thesis is developed in three sections, the first is an overview of the Cryptocurrencies 's world, the second is an explanation of how Decision trees works and a mayor focus on Gradient Boosting. The last section is the practical part, where there is the application of Gradient Boosting to the price forecasting of
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Leitenstorfer, Florian. "Boosting in nonparametric regression : constrained and unconstrained modeling approaches /." München : Hut, 2008. http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&doc_number=016367575&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA.

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Leitenstorfer, Florian. "Boosting in nonparametric regression constrained and unconstrained modeling approaches." München Verl. Dr. Hut, 2007. http://d-nb.info/987775812/04.

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Crudelini, Miriam. "Demand Forecasting mediante algoritmi di boosting: una valutazione sperimentale." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2019. http://amslaurea.unibo.it/19136/.

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Nell'epoca in cui viviamo, grazie ai dispositivi a nostra disposizione, ognuno di noi è produttore di una grande mole di dati, all'interno dei quali sono racchiuse importanti informazioni. Il processo di analisi ed estrazione della conoscenza permette di ottenere importanti informazioni. Un'utilizzo di tali informazioni si ha nel demand forecasting, ossia il processo di previsione della domanda. In questa tesi verranno analizzate alcune metodologie per effettuare previsioni sulla domanda di un prodotto, concentrandosi su una tipologia di algoritmi spesso utilizzati in questo ambito. Sono
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Johannsson, Dagur Valberg. "Biomedical Information Retrieval based on Document-Level Term Boosting." Thesis, Norwegian University of Science and Technology, Department of Computer and Information Science, 2009. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-8981.

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<p>There are several problems regarding information retrieval on biomedical information. The common methods for information retrieval tend to fall short when searching in this domain. With the ever increasing amount of information available, researchers have widely agreed on that means to precisely retrieve needed information is vital to use all available knowledge. We have in an effort to increase the precision of retrieval within biomedical information created an approach to give all terms in a document a context weight based on the contexts domain specific data. We have created a means of i
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Wamhoff, Jons-Tobias, Stephan Diestelhorst, Christof Fetzer, Patrick Marlier, Pascal Felber, and Dave Dice. "Selective Core Boosting: The Return of the Turbo Button." Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2013. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-127748.

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Several modern multi-core architectures support the dynamic control of the CPU's clock rate, allowing processor cores to temporarily operate at speeds exceeding the operational base frequency. Conversely, cores can operate at a lower speed or be disabled altogether to save power. Such facilities are notably provided by Intel's Turbo Boost and AMD's Turbo CORE technologies. Frequency control is typically driven by the operating system which requests changes to the performance state of the processor based on the current load of the system. In this paper, we investigate the use of dynamic frequen
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Brockhaus, Sarah [Verfasser], and Sonja [Akademischer Betreuer] Greven. "Boosting functional regression models / Sarah Brockhaus ; Betreuer: Sonja Greven." München : Universitätsbibliothek der Ludwig-Maximilians-Universität, 2016. http://d-nb.info/1115144812/34.

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48

Redpath, David Bruce. "Boosting with Feature Selection applied to underwater video classification." Thesis, Heriot-Watt University, 2006. http://hdl.handle.net/10399/155.

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49

McGinley, Susan. "Boosting Lycopene in the Diet: The Tomato Consumption Study." College of Agriculture and Life Sciences, University of Arizona (Tucson, AZ), 2006. http://hdl.handle.net/10150/622177.

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50

Ahlgren, Marcus. "Claims Reserving using Gradient Boosting and Generalized Linear Models." Thesis, KTH, Matematisk statistik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-229406.

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Abstract:
One fundamental function of an insurance company revolves around calculating the expected claims costs for which the insurer has to compensate its policyholders for. This is the process of claims reserving which is practised by actuaries using statistical methods. Over the last few decades statistical learning methods have become increasingly popular due to their ability to find complex patterns in any type of data. However, they have not been widely adapted within the insurance sector. In this thesis we evaluate the capability of claims reserving with the method of gradient boosting, a non-pa
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