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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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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 Vector Machine (SVM). The Gabor wavelet transform is used to extract features which describe texture variations of human faces. The Adaboost algorithm is used to select most significant features which represent different individuals. The SVM constructs a classifier with high recognition accuracy. Within the AdaBoost algorithm, a novel weak learner - Potsu is designed. The Potsu weak learner is fast due to the simple perception prototype, and is accurate due to large number of training examples available. More importantly, the Potsu weak learner is the only weak learner which satisfies the requirement of AdaBoost. The Potsu weak learners also demonstrate superior performance over other weak learners, such as FLD. The Gabor-Boosting face recognition algorithm is extended into multi-class classification domain, in which a multi-class weak learner called mPotsu is developed. The experiments show that performance is improved by applying loosely controlled face recognition in the multi-class classification.
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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 Recomendação baseado em Filtragem Colaborativa, técnica cuja essência está na troca de experiências entre usuários com interesses comuns. Na Filtragem Colaborativa, os usuários pontuam cada item experimentado de forma a indicar sua relevância, permitindo que outros do mesmo grupo se beneficiem destas pontuações. Nosso objetivo é utilizar um algoritmo de Boosting para otimizar a performance dos Sistemas de Recomendação. Para isto, utilizamos uma base de dados de anúncios com fins de validação e uma base de dados de filmes com fins de teste. Após adaptações nas estratégias convencionais de Boosting, alcançamos melhorias de até 3% sobre a performance do algoritmo original.With the amount of information and its easy availability on the Internet, many options are offered to the people and they, normally, have little or almost no experience to decide between the existing alternatives. In this scene, the Recommendation Systems appear to organize and recommend automatically, through Machine Learning, the interesting items. One of the great recommendation challenges is to match correctly what is being recommended and who are receiving the recommendation. This work presents a Recommendation System based on Collaborative Filtering, technique whose essence is the exchange of experiences between users with common interests. In Collaborative Filtering, users rate each experimented item indicating its relevance allowing the use of ratings by other users of the same group. Our objective is to implement a Boosting algorithm in order to optimize a Recommendation System performance. For this, we use a database of advertisements with validation purposes and a database of movies with testing purposes. After adaptations in the conventional Boosting strategies, improvements of 3% were reached over the original algorithm.
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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 compromesso tra prestazioni e costi, mentre per migliorare il meccanismo di scintillazione viene proposto cambio di paradigma. Questa tesi di dottorato ha esplorato l’utilizzo di strutture diffrangenti e quantum dots per superare rispettivamenti i limiti legati alla presenza di un angolo critico e alla ricombinazione classica elettrone-lacuna.
I cristalli fotonici utilizzati come reticoli di diffrazione depositati sulla superficie di lettura di scintillatori inorganici hanno mostrato risultati promettenti dal punto di vista di risoluzione energetica e temporale. I modi di diffrazione creati dalla nano-strutturazione periodica creano nuovi gradi di libertà per la luce incidente, entro I quali, con l’utilizzo di programmi di simulazione, si possono trovare soluzioni con un guadagno relativo alla configurazione classica. Un miglioramento è stato dimostrato sperimentalmente per scintillatori misurati in diverse configurazioni.
Nanocristalli sono stati invece utilizzati per migliorare lo stato dell’arte per quanto riguarda le caratteristiche temporali della rivelazione, portando a tempi di decadimento dell’ordine dei picosecondi. L’utilizzo di quatum dots ha permesso di ottimizzare I processi di ricombinazione in scintillatori semiconduttori, portando all’inibizione di canali non radiativi e ad un incremento dell’emissione di dipolo.During this Ph.D., state-of-the-art scintillating materials have been intensively studied with several constraints found regarding their light emission, optical properties, and chemical stability. Different characterization benches were developed specifically for the measurements presented in the thesis and extensive work has been dedicated to fine tune the simulations framework that describes scintillators and photo-detectors. Classical approaches were found to be already at a good trade-off between performances and costs while to really boost scintillation detection a shift in paradigm was needed, moving away from classical ray tracing concepts and scintillation mechanism. This Ph.D. work explored the use of diffraction and quantum dots to break the limit of critical angle and classical e-h recombination, respectively. \newline Photonic crystals were used as diffracting layer deposited on the read-out face of inorganic scintillators and showed promising results from the point of view the crystal's time and energy resolution. The additional modes provided by the periodical nano-structuration of the read-out face add several degrees of freedom where simulations could find new optimal solutions. An enhanced extraction of scintillation light was demonstrated in different crystal configurations.\newline Nanocrystals, on the other hand, pushed the state-of-the-art of scintillation timing properties down to the ps scale, bringing innovative ideas for future fast detectors. The use of quantum dots allowed to tune the recombination mechanism of scintillating semiconductors leading to inhibited non-radiative channels and enhance dipole emission from the emitting centers.
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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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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. Die Arbeit behandelt folgende Sachverhalte:
o Die zur Analyse von Boosting-Methoden geeignete Statistische Lerntheorie. Wir studieren lerntheoretische Garantien zur Abschätzung der Vorhersagequalität auf ungesehenen Mustern. Kürzlich haben sich sogenannte Klassifikationstechniken mit großem Margin als ein praktisches Ergebnis dieser Theorie herausgestellt - insbesondere Boosting und Support-Vektor-Maschinen. Ein großer Margin impliziert eine hohe Vorhersagequalität der Entscheidungsregel. Deshalb wird analysiert, wie groß der Margin bei Boosting ist und ein verbesserter Algorithmus vorgeschlagen, der effizient Regeln mit maximalem Margin erzeugt.
o Was ist der Zusammenhang von Boosting und Techniken der konvexen Optimierung?
Um die Eigenschaften der entstehenden Klassifikations- oder Regressionsregeln zu analysieren, ist es sehr wichtig zu verstehen, ob und unter welchen Bedingungen iterative Algorithmen wie Boosting konvergieren. Wir zeigen, daß solche Algorithmen benutzt werden koennen, um sehr große Optimierungsprobleme mit Nebenbedingungen zu lösen, deren Lösung sich gut charakterisieren laesst. Dazu werden Verbindungen zum Wissenschaftsgebiet der konvexen Optimierung aufgezeigt und ausgenutzt, um Konvergenzgarantien für eine große Familie von Boosting-ähnlichen Algorithmen zu geben.
o Kann man Boosting robust gegenüber Meßfehlern und Ausreissern in den Daten machen?
Ein Problem bisheriger Boosting-Methoden ist die relativ hohe Sensitivität gegenüber Messungenauigkeiten und Meßfehlern in der Trainingsdatenmenge. Um dieses Problem zu beheben, wird die sogenannte 'Soft-Margin' Idee, die beim Support-Vector Lernen schon benutzt wird, auf Boosting übertragen. Das führt zu theoretisch gut motivierten, regularisierten Algorithmen, die ein hohes Maß an Robustheit aufweisen.
o Wie kann man die Anwendbarkeit von Boosting auf Regressionsprobleme erweitern?
Boosting-Methoden wurden ursprünglich für Klassifikationsprobleme entwickelt. Um die Anwendbarkeit auf Regressionsprobleme zu erweitern, werden die vorherigen Konvergenzresultate benutzt und neue Boosting-ähnliche Algorithmen zur Regression entwickelt. Wir zeigen, daß diese Algorithmen gute theoretische und praktische Eigenschaften haben.
o Ist Boosting praktisch anwendbar?
Die dargestellten theoretischen Ergebnisse werden begleitet von Simulationsergebnissen, entweder, um bestimmte Eigenschaften von Algorithmen zu illustrieren, oder um zu zeigen, daß sie in der Praxis tatsächlich gut funktionieren und direkt einsetzbar sind. Die praktische Relevanz der entwickelten Methoden wird in der Analyse chaotischer Zeitreihen und durch industrielle Anwendungen wie ein Stromverbrauch-Überwachungssystem und bei der Entwicklung neuer Medikamente illustriert.
In this work we consider statistical learning problems. A learning machine aims to extract information from a set of training examples such that it is able to predict the associated label on unseen examples. We consider the case where the resulting classification or regression rule is a combination of simple rules - also called base hypotheses. The so-called boosting algorithms iteratively find a weighted linear combination of base hypotheses that predict well on unseen data. We address the following issues:
o The statistical learning theory framework for analyzing boosting methods.
We study learning theoretic guarantees on the prediction performance on unseen examples. Recently, large margin classification techniques emerged as a practical result of the theory of generalization, in particular Boosting and Support Vector Machines. A large margin implies a good generalization performance. Hence, we analyze how large the margins in boosting are and find an improved algorithm that is able to generate the maximum margin solution.
o How can boosting methods be related to mathematical optimization techniques?
To analyze the properties of the resulting classification or regression rule, it is of high importance to understand whether and under which conditions boosting converges. We show that boosting can be used to solve large scale constrained optimization problems, whose solutions are well characterizable. To show this, we relate boosting methods to methods known from mathematical optimization, and derive convergence guarantees for a quite general family of boosting algorithms.
o How to make Boosting noise robust?
One of the problems of current boosting techniques is that they are sensitive to noise in the training sample. In order to make boosting robust, we transfer the soft margin idea from support vector learning to boosting. We develop theoretically motivated regularized algorithms that exhibit a high noise robustness.
o How to adapt boosting to regression problems?
Boosting methods are originally designed for classification problems. To extend the boosting idea to regression problems, we use the previous convergence results and relations to semi-infinite programming to design boosting-like algorithms for regression problems. We show that these leveraging algorithms have desirable theoretical and practical properties.
o Can boosting techniques be useful in practice?
The presented theoretical results are guided by simulation results either to illustrate properties of the proposed algorithms or to show that they work well in practice. We report on successful applications in a non-intrusive power monitoring system, chaotic time series analysis and a drug discovery process.
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Anmerkung:
Der Autor ist Träger des von der Mathematisch-Naturwissenschaftlichen Fakultät der Universität Potsdam vergebenen Michelson-Preises für die beste Promotion des Jahres 2001/2002.
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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.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 53-56).
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 instrumental to its popularity is its resistance to overfitting. Previous experiments provide a margin-based explanation for this resistance to overfitting. In this thesis, the main finding is that boosting's resistance to overfitting can be understood in terms of how it handles noisy (mislabeled) points. Confirming experimental evidence emerged from experiments using the Wisconsin Diagnostic Breast Cancer(WDBC) dataset commonly used in machine learning experiments. A majority vote ensemble filter identified on average that 2.5% of the points in the dataset as noisy. The experiments chiefly investigated boosting's treatment of noisy points from a volume-based perspective. While the cell volume surrounding noisy points did not show a significant difference from other points, the decision volume surrounding noisy points was two to three times less than that of non-noisy points. Additional findings showed that decision volume not only provides insight into boosting's resistance to overfitting in the context of noisy points, but also serves as a suitable metric for identifying which points in a dataset are likely to be mislabeled.
by Jeffrey Chan.
M. Eng.
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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 proven mathematicallyand verified in a series of identification experiments that TM-Boost,under reasonable conditions, converges to the true underlying system.Inom området maskininlärning har algoritmen Adaptive Boosting framgångs-rikt använts på många typer av klassificerings- och regressionsproblem. Hit-intills har algoritmen dock inte använts till att estimera dynamiska system. I detta examensarbete utforskas sambanden mellan Adaptive Boosting och sys-temidentifiering. En ny identifieringsmetod kallad TM-Boost, som är inspir-erad av Adaptive Boosting introduceras. Metoden baseras på ortonormala basfunktioner och bygger iterativt upp ett dynamiskt system. En tilltalande egenskap är att det inte längre är nödvändigt att specifiera modellordning. Det bevisas också matematiskt att det estimerade systemet, under vissa förut-sättningar, konvergerar mot det sanna underliggande systemet, vilket även verifieras i en serie identifieringsexperiment.
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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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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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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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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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Para compor um sistema de Reconhecimento Automático de Voz, pode ser utilizada uma tarefa chamada Classificação Fonética, onde a partir de uma amostra de voz decide-se qual fonema foi emitido por um interlocutor. Para facilitar a classificação e realçar as características mais marcantes dos fonemas, normalmente, as amostras de voz são pré- processadas através de um fronl-en'L Um fron:-end, geralmente, extrai um conjunto de parâmetros para cada amostra de voz. Após este processamento, estes parâmetros são insendos em um algoritmo classificador que (já devidamente treinado) procurará decidir qual o fonema emitido. Existe uma tendência de que quanto maior a quantidade de parâmetros utilizados no sistema, melhor será a taxa de acertos na classificação. A contrapartida para esta tendência é o maior custo computacional envolvido. A técnica de Seleção de Parâmetros tem como função mostrar quais os parâmetros mais relevantes (ou mais utilizados) em uma tarefa de classificação, possibilitando, assim, descobrir quais os parâmetros redundantes, que trazem pouca (ou nenhuma) contribuição à tarefa de classificação. A proposta deste trabalho é aplicar o classificador SVM à classificação fonética, utilizando a base de dados TIMIT, e descobrir os parâmetros mais relevantes na classificação, aplicando a técnica Boosting de Seleção de Parâmetros.
With the aim of setting up a Automatic Speech Recognition (ASR) system, a task named Phonetic Classification can be used. That task consists in, from a speech sample, deciding which phoneme was pronounced by a speaker. To ease the classification task and to enhance the most marked characteristics of the phonemes, the speech samples are usually pre-processed by a front-end. A front-end, as a general rule, extracts a set of features to each speech sample. After that, these features are inserted in a classification algorithm, that (already properly trained) will try to decide which phoneme was pronounced. There is a rule of thumb which says that the more features the system uses, the smaller the classification error rate will be. The disadvantage to that is the larger computational cost. Feature Selection task aims to show which are the most relevant (or more used) features in a classification task. Therefore, it is possible to discover which are the redundant features, that make little (or no) contribution to the classification task. The aim of this work is to apply SVM classificator in Phonetic Classification task, using TIMIT database, and discover the most relevant features in this classification using Boosting approach to implement Feature Selection.
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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 criticism. As a result, numerous cost-sensitive modifications of the original algorithm have been proposed. Each of these has its own motivations, and its own claims to superiority. With a thorough analysis of the literature 1997-2016, we find 15 distinct cost-sensitive Boosting variants - discounting minor variations. We critique the literature using {\em four} powerful theoretical frameworks: Bayesian decision theory, the functional gradient descent view, margin theory, and probabilistic modelling. From each framework, we derive a set of properties which must be obeyed by boosting algorithms. We find that only 3 of the published Adaboost variants are consistent with the rules of all the frameworks - and even they require their outputs to be calibrated to achieve this. Experiments on 18 datasets, across 21 degrees of cost asymmetry, all support the hypothesis - showing that once calibrated, the three variants perform equivalently, outperforming all others. Our final recommendation - based on theoretical soundness, simplicity, flexibility and performance - is to use the original Adaboost algorithm albeit with a shifted decision threshold and calibrated probability estimates. The conclusion is that novel cost-sensitive boosting algorithms are unnecessary if proper calibration is applied to the original.
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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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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-guerre et de la Seconde Guerre mondiale et celle de l'après-guerre. Le message réformateur et égalitaire de la "guerre du peuple" fut progressivement remplacé par une image plus conservatrice et élitiste de la Grande-Bretagne. L'analyse thématique dans la troisième partie permet de mieux appréhender cette mutation dans le discours de Britain To-day et révèle l'ambivalence de le revue face à la Grande-Bretagne moderne.
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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 structures métriques fortes pourront stimuler l'apprentissage implicite. Nous faisons l'hypothèse que le fait de présenter des hauteurs de notes avec des structures métriques fortes permet de développer des attentes temporelles par rapport à l'arrivée du prochain évènement. Ces attentes facilitent le traitement de hauteur des notes et ensuite «boostent» l'apprentissage implicite de la grammaire artificielle. Trois études ont été réalisées pendant cette thèse. L'étude 1 était une étude comportementale dans laquelle nous avons étudié l'influence d'une présentation temporelle régulière (avec une métrique forte) vs. irrégulière sur l'apprentissage implicite d'une grammaire artificielle basée sur des structures de hauteur des notes. Les résultats ont montré que la présentation temporelle influence l'apprentissage implicite et que la présentation temporelle régulière représente un avantage pour l'apprentissage implicite par rapport à la présentation temporelle irrégulière. Dans une étude électrophysiologique (L'étude 2) nous avons étudié quelle présentation temporelle de la grammaire artificielle, rythmique avec une métrique forte ou isochrone, serait plus efficace pour apprendre implicitement la grammaire des hauteurs des notes. Les résultats électrophysiologiques ont montré que les structures métriques apportent un bénéfice supplémentaire à l'apprentissage implicite. Dans l'étude 3 nous avons étudié comment des structures métriques fortes permettent d'améliorer les capacités d'apprentissage implicite chez des patients avec des lésions dans le cortex frontal inférieur qui ont été décrits comme déficitaires pour apprendre des structures artificielles. Les résultats comportementaux et électrophysiologiques ont montré que les patients atteints de lésions dans le cortex frontal inférieur sont capables d'apprendre une nouvelle grammaire artificielle malgré leurs lésions et leur déficit syntaxique. Il pourrait être utile d'exploiter cet avantage de la présentation métrique chez les patients, pour qui un déficit de l'apprentissage implicite a été montré avec des matériaux non-métriques et non musicauxThe thesis aims to investigate how temporal regularities can influence the implicit learning of artificial pitch structures. Implicit learning refers to the acquisition of structure knowledge by mere exposure. According to the Dynamic Attending Theory proposed by Jones (Jones, 1976), internal attentional oscillators synchronize with external temporal regularities, helping to guide attention over time and to develop temporal and perceptual expectations about future events. We made the hypothesis that strongly metrical structures might boost implicit learning, and in particular, that the strongly metrical presentation of pitch structures helps listeners to develop temporal expectations about the occurrence of the next event and thus benefits to the processing of the pitch dimension, leading to better learning of the artificial material. Three studies were realized during this PhD thesis. In Study 1, we used a behavioral approach to investigate how regular and irregular temporal presentations of an artificial pitch grammar influence implicit learning. The data revealed that both types of temporal presentations can influence implicit learning, but that the regular presentation leads to an advantage over the irregular presentation. In Study 2, we used behavioral and electrophysiological methods to investigate which type of regular temporal presentation of the artificial grammar, i.e. strongly metrical or isochronous, leads to better implicit learning of pitch structures. Electrophysiological results showed that the metrical framework provided an additional benefit for the pitch structure learning. In Study 3, we investigated whether the strongly metrical presentation allows patients with left inferior frontal lesions (with previously reported deficits for implicit learning) to learn the artificial pitch grammar. Behavioral and electrophysiological results showed that patients with left inferior frontal gyrus lesions acquired the new artificial grammar despite their lesions and despite previously reported deficits in implicit learning and syntax processing of natural language. It might be useful to exploit the potential benefit of the strongly metrical presentation further in patients for who impaired IL has been shown with non-musical and non-metrical materials
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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.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 adaptative et itérative des hypothèses de base en focalisant l'apprentissage, à chaque nouvelle itération, sur les exemples qui ont été difficiles à apprendre lors des itérations précédentes. Cependant, AdaBoost est relativement inadapté aux données du monde réel. Dans cette thèse, nous nous concentrons en particulier sur les données bruitées, et sur les données hétérogènes.
Dans le cas des données bruitées, non seulement la méthode peut devenir très lente, mais surtout, AdaBoost apprend par coeur les données, et le pouvoir prédictif des hypothèses globales générées, s'en trouve extrêmement dégradé. Nous nous sommes donc intéressés à une adaptation du boosting pour traiter les données bruitées. Notre solution exploite l'information provenant d'un oracle de confiance permettant d'annihiler les effets dramatiques du bruit. Nous montrons que notre nouvel algorithme conserve les propriétés théoriques du boosting standard. Nous mettons en pratique cette nouvelle méthode, d'une part sur des données numériques, et d'autre part, de manière plus originale, sur des données textuelles.
Dans le cas des données hétérogènes, aucune adaptation du boosting n'a été proposée jusqu'à présent. Pourtant, ces données, caractérisées par des attributs multiples mais de natures différentes (comme des images, du son, du texte, etc), sont extrêmement fréquentes sur le web, par exemple. Nous avons donc développé un nouvel algorithme de boosting permettant de les utiliser. Plutôt que de combiner des hypothèses boostées indépendamment, nous construisons un nouveau schéma de boosting permettant de faire collaborer durant l'apprentissage des algorithmes spécialisés sur chaque type d'attribut. Nous prouvons que les décroissances exponentielles des erreurs sont toujours assurées par ce nouveau modèle, aussi bien d'un point de vue théorique qu'expérimental.
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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 généralisation d'algorithmessélectionnant un estimateur au sein d'une classe massive telle que l'enveloppe convexe d'une classe de VC. Dans le premier volet du mémoire, on rappelle les interprétations des algorithmes de boosting comme des implémentations de principes de minimisation
de risques convexes et on étudie leurs propriétés sous cet angle. En particulier, on montre l'importance de la
régularisation pour obtenir des stratégies consistantes. On développe également une nouvelle classe d'algorithmes de type gradient stochastique appelés algorithmes de descente miroir avec moyennisation et on évalue leur comportement à travers des simulations informatiques. Après avoir présenté les principes fondamentaux du boosting, on s'attache dans le
deuxième volet à des questions plus avancées telles que
l'élaboration d'inégalités d'oracle. Ainsi, on étudie la
calibration précise des pénalités en fonction des critères
de coût utilisés. On présente des résultats
non-asymptotiques sur la performance des estimateurs du boosting pénalisés, notamment les vitesses rapides sous les conditions de marge de type Mammen-Tsybakov et on décrit les capacités d'approximation du boosting utilisant les "rampes" (stumps) de décision. Le troisième volet du mémoire explore le problème du ranking. Un enjeu important dans des applications
telles que la fouille de documents ou le "credit scoring" est d'ordonner les instances plutôt que de les catégoriser. On propose une formulation simple de ce problème qui permet d'interpréter le ranking comme une classification sur des paires d'observations. La différence dans ce cas vient du fait que les
critères empiriques sont des U-statistiques et on développe donc la théorie de la classification adaptée à ce contexte. On explore également la question de la généralisation de l'erreur de ranking afin de pouvoir inclure des a priori sur l'ordre des instances, comme dans le cas où on ne s'intéresse qu'aux "meilleures" instances.
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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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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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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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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.Cataloged from PDF version of thesis.
Includes bibliographical references (pages 153-178).
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 Illustris simulation, and find that Galactic indirect detection signals are expected to be largely symmetric, while extragalactic signals are not, due to recent mergers and the presence of substructure. Second, through the study of the high resolution Milky Way simulation Eris, I find that metal-poor halo stars can be used as tracers for the dark matter velocity distribution. I use the Sloan Digital Sky Survey to obtain the first empirical velocity distribution of dark matter, which weakens the expected direct detection limits by up to an order of magnitude at masses by Lina Necib.
Ph. D.
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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.Includes bibliographical references (p. 79-84).
by Raj Dharmarajan Iyer, Jr.
S.M.
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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'arbres de décision. Nous généralisons ce concept en proposant un algorithme pour booster» les performances d' une classe assez large de classifieurs. Enfin, nous nous interessons aux interactions possibles de ces deux types d'apprentissage. Des résultats théoriques mais aussi expérimentaux étayent notre proposThe machine learning is an important field of investigation in artificial intelligence. We consider it under his two aspects supervised and not-supervised, respectively via two techniques : the spectral clustering and the boosting. The spectral clustering which is based on algebraic results proved to be simple to use and effective. We propose a probabilistic interpretation of this method and an application to the distinction of languages in a multilingual corpus of texts. The boosting is a learning technology making it possible to increase the performances of decision trees. We generalize this concept by proposing an algorithm for enhancing the performances of a rather broad class classifieurs. Lastly, we are interested in the possible interactions of these two types of training. Theoretical but such experimental results support our matter
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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 method to enhance memory and speech-perception in humans.
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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 TelemáticaAtribuição 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.
Authorship attribution is the task of assigning an author to an anonymous document. Although the task was traditionally performed by expert linguists, many new techniques have been suggested since the appearance of computers, in the middle of the XX century, some of them using compressors to find repeating patterns in the data. This work will present the results that can be achieved by a collaboration of more than one compressor using a meta-algorithm known as Boosting.
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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 & 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 learning from positive-only data has traditionally been difficult. An empirical methodology is described and deployed for testing positive-only learning systems using commonly available multiclass datasets to compare these learning systems with each other and with multiclass learning systems. Empirical results show that our positive-only boosting-like method learns, using stumps as a base learner and from positive data only, successfully, and in the process does not pay too heavy a price in accuracy compared to learners that have access to both positive and negative data. We also describe methods of using positive-only learners on multiclass learning tasks and vice versa and empirically demonstrate the superiority of our method of learning in a boosting-like fashion from positive-only data over a traditional multiclass learner converted to learn from positive-only data. Finally we examine some alternative frameworks, such as when additional unlabelled training examples are given. Some theoretical justifications of the results and methods are also provided.
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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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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 this dissertation, we design autoregressive time series models based on boosting which capture nonlinearity in the mean and in the variance, and propose new models for multi-step forecasting of both.
We use a special version of boosting, called componentwise gradient boosting, which is innovative in the estimation of the conditional variance of asset returns by sorting out irrelevant (lagged) predictors. We propose a model which enables us not only to identify the factors which drive market volatility, but also to assess the specific nature of their impact. Therefore, we gain a deeper insight into the nature of the volatility processes. We analyze four broad asset classes, namely, stocks, commodities, bonds, and foreign exchange, and use a wide range of potential macro and financial drivers. The proposed model for volatility forecasting performs very favorably for stocks and commodities relative to the common GARCH(1,1) benchmark model. The advantages are particularly convincing for longer forecasting horizons. To our knowledge, the application of boosting to multi-step forecasting of either the mean or the variance has not been done before.
In a separate study, we focus on the conditional mean of German industrial production. With boosting, we improve the forecasting accuracy when compared to several competing models including the benchmark in this field, the linear autoregressive model. In an exhaustive simulation study we show that boosting of high-order nonlinear autoregressive time series can be very competitive in terms of goodness-of-fit when compared to alternative nonparametric models.
Finally, we apply boosting in a spatio-temporal context to data coming from outside the econometric field. We estimate the browsing pressure on young beech trees caused by the game species within the borders of the Bavarian Forest National Park ``Bayerischer Wald,'' Germany. We found that using the geographic coordinates of the browsing cases contributes considerably to the fit. Furthermore, this bivariate geographic predictor is better suited for prediction if it allows for abrupt changes in the browsing pressure.
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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 JANEIROCOORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR
INSTITUTO MILITAR DE ENGENHARIA
CENTRO TECNOLÓGICO DO EXÉRCITO
PROGRAMA DE EXCELENCIA ACADEMICA
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 algoritmo aumenta os pesos dos exemplos em que os classificadores anteriores cometeram erros. Assim, foca o classificador adicional nos exemplos mais difíceis. Inicialmente, uma distribuição uniforme de pesos é atribúda aos exemplos. Entretanto, não existe garantia que essa seja a melhor escolha para a distribuição inicial. Neste trabalho, apresentamos o Boosting at Start (BAS), uma nova abordagem de aprendizado de máquina baseada em Boosting. O BAS generaliza o AdaBoost permitindo a utilização de uma distribuição inicial arbitrária. Também apresentamos esquemas para determinação de tal distribuição. Além disso, mostramos como adaptar o BAS para esquemas de Aprendizado Semi-supervisionado. Adicionalmente, descrevemos a aplicação do BAS em diferentes problemas de classificação de dados e de texto, comparando o seu desempenho com o algoritmo AdaBoost original e alguns algoritmos do estado-da-arte para tais tarefas. Os resultados experimentais indicam que uma modelagem simples usando o algoritmo BAS gera classificadores eficazes.
Boosting is a Machine Learning technique that combines several weak classifers with the goal of improving the overall accuracy. In each iteration, the algorithm updates the example weights and builds an additional classifer. A simple voting scheme is used to combine the classifers. The most famous Boosting-based algorithm is AdaBoost. This algorithm increases the weights of the examples that were misclassifed by the previous classifers. Thus, it focuses the additional classifer on the hardest examples. Initially, an uniform weight distribution is assigned to the examples. However, there is no guarantee that this is the best choice for the initial distribution. In this work, we present Boosting at Start (BAS), a new Machine Learning approach based on Boosting. BAS generalizes AdaBoost by allowing the use of an arbitrary initial distribution. We present schemes for the determination of such distribution. We also show how to adapt BAS to Semi-supervised learning schemes. Additionally, we describe the application of BAS in different problems of data and text classifcation, comparing its performance with the original AdaBoost algorithm and some state-of-the-art algorithms for such tasks. The experimental results indicate that a simple modelling using the BAS algorithm generates effective classifers.
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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 imbalanced multi-class data can be found in face recognition, where tens of classes exist, and in capsule endoscopy, which suffers massive imbalance between the classes. This dissertation introduces RegBoost, a new boosting framework to address the imbalanced, multi-class problems. This method applies a weighted stratified sampling technique and incorporates a regularization term that accommodates multi-class data sets and automatically determines the error bound of each base classifier. The regularization parameter penalizes the classifier when it misclassifies instances that were correctly classified in the previous iteration. The parameter additionally reduces the bias towards majority classes. Experiments are conducted using 12 diverse data sets with moderate to high imbalance ratios. The results demonstrate superior performance of the proposed method compared to several state-of-the-art algorithms for imbalanced, multi-class classification problems. More importantly, the sensitivity improvement of the minority classes using RegBoost is accompanied with the improvement of the overall accuracy for all classes. With unpredictability regularization, a diverse group of classifiers are created and the maximum accuracy improvement reaches above 24%. Using stratified undersampling, RegBoost exhibits the best efficiency. The reduction in computational cost is significant reaching above 50%. As the volume of training data increase, the gain of efficiency with the proposed method becomes more significant.
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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 PrevisãoA 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 capita dos municípios (que ainda não tinham oferta de ensino superior). Mais precisamente, o impacto de introduzir IES foi, em média, de aproximadamente 4%, enquanto que o impacto de dobrar o número de pessoas com ensino superior foi também de 4%. O modelo aponta que selection bias está fortemente presente nos dados.
The present thesis studies the impact on income per capita of the opening of new Higher Education Institutions (HEI) in Brazilian municipalities where there was no supply of higher education. To do so, I constructed a panel with data from 2000 and 2010 with all Brazilian municipalities, based on the Demographic Census and on the Higher Education Census. In order to overcome the endogeneity problems normally encountered in these datasets, I applied the Heckman model. The results of the Heckman model point to a positive impact of new HEI openings in municipalities where there was no higher education supply in the initial period. The impact of introducing HEI in the municipality is of around 4% on income per capita, while the impact of doubling the proportion of the population with tertiary education is, as well, of around 4%. The model suggests that selection bias highly present in the dataset.
info:eu-repo/semantics/publishedVersion
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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 infrastructure deficits in the hope that it will ultimately spur economic growth and attract more investment. SSA's access to the international markets has grown significantly, facilitated by easing global financial conditions. By end March 2014, 13 countries had issued international sovereign bonds, for reasons that include infrastructure building. The sub-Saharan Africa's region's access to international markets has come under much attention lately as debt levels are rising with fears that they may reach the unsustainable pre –HIPC levels. For example, Zambia's total debt burden stood at an unsustainably high USD5.4 billion in 2005 – equivalent to 74% of the country's GDP and almost 208% of its foreign exchange earnings (IHS Global Insights, 2014). The attainment of debt relief under the IMF and World Bank's Multilateral Debt Relief Initiative (MDRI) in early 2006 dramatically decreased the country's debt holdings to less than 25% of GDP. However, in the third quarter of 2012, the government issued its first Eurobond and raised debt capital of USD 750 million. This was followed by a USD 1 billion Eurobond issue in the second quarter of 2014 (IHS Global Insights, 2014) with the stated intention of using the funds for infrastructural development and maintenance. However, according to the latest IMF statement on Zambia released on 6 June 2014, Zambia's macroeconomic situation, though potentially promising, is in trouble and needs urgent fixing. It appears that the government of Zambia wants an IMF funding arrangement, possibly a bailout (Zambian Economist, 2014). In the face of mounting evidence that access to the international capital markets and rising public debt are more likely to have enhanced vulnerability than growth, this paper examines the determinants of economic growth in a panel of 15 countries. It examines the impact of external debt, total public debt and infrastructure expenditure on economic growth in the southern African region over a period of 10 years (between 2004 and 2014). The findings suggest an inverse relationship between external debt and total public debt against economic growth. The findings also suggest that there is a positive relationship between infrastructure development and economic growth amongst the countries in the southern Africa region. These relationships were found to be insignificant, suggesting that other factors outside of the variables of infrastructure expenditure, external debt and total public debt are influencing economic growth (or slowdown) in the region. The paper also examines the current debt situation in the 15 countries and policy considerations are also presented.
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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>. "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 cryptocurrencies and then the application of the same algorithm to flat currencies. The aim is to find out if the performance of Gradient Boosting is better for cryptocurrencies forecasting or flat currencies.
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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 stati proposti e valutati tre algoritmi di machine learning basati sul boosting. Per migliorare le prestazioni è stata implementata un fase iniziale di ottimizzazione dei modelli. Infine, i modelli costruiti sono stati testati ed è stata effettuata un'analisi delle relative prestazioni.
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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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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 including our context weights in document ranking, by combining the weights with existing ranking models. Combining context weights with existing models has given us document-level term boosting, where the context of the queried terms within a document will positively or negatively affect the documents ranking score. We have tested out our approach by implementing a full search engine prototype and evaluatied it on a document collection within biomedical domain. Our work shows that this type of score boosting has little effect on overall retrieval precision. We conclude that the approach we have created, as implemented in our prototype, not to necessarily be good means of increasing precision in biomedical retrieval systems.
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Wamhoff, Jons-Tobias, Stephan Diestelhorst, Christof Fetzer, Patrick Marlier, Pascal Felber i 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 frequency scaling from user space to speed up multi-threaded applications that must occasionally execute time-critical tasks or to solve problems that have heterogeneous computing requirements. We propose a general-purpose library that allows selective control of the frequency of the cores - subject to the limitations of the target architecture. We analyze the performance trade-offs and illustrate its benefits using several benchmarks and real-world workloads when temporarily boosting selected cores executing time-critical operations. While our study primarily focuses on AMD's architecture, we also provide a comparative evaluation of the features, limitations, and runtime overheads of both Turbo Boost and Turbo CORE technologies. Our results show that we can successful exploit these new hardware facilities to
accelerate the execution of key sections of code (critical paths) improving overall performance of some multi-threaded applications. Unlike prior research, we focus on performance instead of power conservation. Our results further can give guidelines for the design of hardware power management facilities and the operating system interfaces to those facilities.
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Brockhaus, Sarah [Verfasser], i 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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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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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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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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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-parametric statistical learning method that has proven to be successful within multiple other disciplines which has made it very popular. The gradient boosting technique is compared with the generalized linear model(GLM) which is widely used for modelling claims. We compare the models by using a claims data set provided by Länsförsäkringar AB which allows us to train the models and evaluate their performance on data not yet seen by the models. The models were implemented using R. The results show that the GLM has a lower prediction error. Also, the gradient boosting method requires more fine tuning to handle claims data properly while the GLM already possesses certain features that makes it suitable for claims reserving without making as many adjustments in the model implementation. The advantage of capturing complex dependencies in data is not fully utilized in this thesis since we only work with 6 predictor variables. It is more likely that gradient boosting can compete with GLM when predicting more complicated claims.En av de centrala verksamheterna ett försäkringsbolag arbetar med handlar om att uppskatta skadekostnader för att kunna ersätta försäkringstagarna. Denna procedur kallas reservsättning och utförs av aktuarier med hjälp av statistiska metoder. Under de senaste årtiondena har statistiska inlärningsmetoder blivit mer och mer populära tack vare deras förmåga att hitta komplexa mönster i alla typer av data. Dock har intresset för dessa varit relativt lågt inom försäkringsbranschen till förmån för mer traditionella försäkringsmatematiska metoder. I den här masteruppsatsen undersöker vi förmågan att reservsätta med metoden \textit{gradient boosting}, en icke-parametrisk statistisk inlärningsmetod som har visat sig fungera mycket väl inom en rad andra områden vilket har gjort metoden mycket populär. Vi jämför denna metod med generaliserade linjära modeller(GLM) som är en av de vanliga metoderna vid reservsättning. Vi jämför modellerna med hjälp av ett dataset tillhandahålls av Länsförsäkringar AB. Modellerna implementerades med R. 80\% av detta dataset används för att träna modellerna och resterande 20\% används för att evaluera modellernas prediktionsförmåga på okänd data. Resultaten visar att GLM har ett lägre prediktionsfel. Gradient boosting kräver att ett antal hyperparametrar justeras manuellt för att få en välfungerande modell medan GLM inte kräver lika mycket korrigeringar varför den är mer praktiskt lämpad. Fördelen med att kunna modellerna komplexa förhållanden i data utnyttjas inte till fullo i denna uppsats då vi endast arbetar med sex prediktionsvariabler. Det är sannolikt att gradient boosting skulle ge bättre resultat med mer komplicerade datastrukturer.