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Relevant bibliographies by topics / Boosting

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Academic literature on the topic 'Boosting'

Author: Grafiati

Published: 4 June 2021

Last updated: 9 June 2025

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Journal articles on the topic "Boosting"

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Singh, Sandeep, and Guy W. Fried. "“Boosting”." Medicine & Science in Sports & Exercise 38, Supplement (May 2006): S479. http://dx.doi.org/10.1249/00005768-200605001-02879.

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Fearn, Tom. "Boosting." NIR news 18, no. 1 (February 2007): 11–12. http://dx.doi.org/10.1255/nirn.1004.

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Becker, Thijs, Melvin Geubbelmans, Axel-Jan Rousseau, Dirk Valkenborg, and Tomasz Burzykowski. "Boosting." American Journal of Orthodontics and Dentofacial Orthopedics 165, no. 1 (January 2024): 122–24. http://dx.doi.org/10.1016/j.ajodo.2023.10.003.

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Bühlmann, Peter, and Bin Yu. "Boosting." WIREs Computational Statistics 2, no. 1 (December 31, 2009): 69–74. http://dx.doi.org/10.1002/wics.55.

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Onoda, Takashi. "Overfitting of boosting and regularized Boosting algorithms." Electronics and Communications in Japan (Part III: Fundamental Electronic Science) 90, no. 9 (2007): 69–78. http://dx.doi.org/10.1002/ecjc.20344.

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Wojtys, Edward M. "Boosting Performance." Sports Health: A Multidisciplinary Approach 13, no. 2 (February 24, 2021): 109–10. http://dx.doi.org/10.1177/1941738121991495.

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Otohwo, I. O., and D. R. Sadoh. "Boosting numbers." British Dental Journal 197, no. 8 (October 2004): 449. http://dx.doi.org/10.1038/sj.bdj.4811778.

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Leigh-Smith, S. "Blood boosting." British Journal of Sports Medicine 38, no. 1 (February 1, 2004): 99–101. http://dx.doi.org/10.1136/bjsm.2003.007195.

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Pereira, M. "Boosting competitiveness." IEE Review 50, no. 5 (May 1, 2004): 35–37. http://dx.doi.org/10.1049/ir:20040504.

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Ellis, Andrew. "Boosting bandwidth." Physics World 29, no. 4 (April 2016): 17. http://dx.doi.org/10.1088/2058-7058/29/4/29.

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Dissertations / Theses on the topic "Boosting"

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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.<br>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.<br>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. <br /> Die Arbeit behandelt folgende Sachverhalte: <br /> <br /> 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.<br /> <br /> o Was ist der Zusammenhang von Boosting und Techniken der konvexen Optimierung? <br /> 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.<br /> <br /> o Kann man Boosting robust gegenüber Meßfehlern und Ausreissern in den Daten machen? <br /> 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.<br /> <br /> o Wie kann man die Anwendbarkeit von Boosting auf Regressionsprobleme erweitern? <br /> 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.<br /> <br /> o Ist Boosting praktisch anwendbar? <br /> 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.<br>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:<br /> <br /> o The statistical learning theory framework for analyzing boosting methods.<br /> 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.<br /> <br /> o How can boosting methods be related to mathematical optimization techniques?<br /> 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.<br /> <br /> o How to make Boosting noise robust?<br /> 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.<br /> <br /> o How to adapt boosting to regression problems?<br /> 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.<br /> <br /> o Can boosting techniques be useful in practice?<br /> 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. <br><br> ---<br> Anmerkung:<br> 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.<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 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.<br>by Jeffrey Chan.<br>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.<br>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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Books on the topic "Boosting"

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Maccaro, Janet C. Brain-boosting foods. Lake Mary, Fla: Siloam, 2008.

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Allen, Kelly-Ann, and Peggy Kern. Boosting School Belonging. Abingdon, Oxon ; New York, NY : Routledge, 2019.: Routledge, 2019. http://dx.doi.org/10.4324/9780203729632.

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Steer, Wilston. Boosting the Bay. North Bay, Ont: North Bay and District Chamber of Commerce, 1994.

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Thompson, Simon Giles. Distributed boosting algorithms. Portsmouth: University of Portsmouth, 1999.

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White, Sandra Sardella. Boosting your energy. Edited by Bennett Hilary, Gilbert Susan 1950-, Dawson, D. M. (David Michael), 1930-, Komaroff Anthony L, Dadoly Ann Marie, and Harvard Medical School. Health Publications Group. Boston, MA: Harvard Health Publications, 2008.

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Schapire, Robert E. Boosting: Foundations and algorithms. Cambridge, MA: MIT Press, 2012.

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Satterfield, Jason M. Boosting your emotional intelligence. Chantilly, VA: Teaching Co., 2017.

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Camarinha-Matos, Luis M., Hamideh Afsarmanesh, and Angel Ortiz, eds. Boosting Collaborative Networks 4.0. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-62412-5.

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Cogen, Victor. Boosting the Adolescent Underachiever. Boston, MA: Springer US, 1992. http://dx.doi.org/10.1007/978-1-4899-6576-9.

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Authority, Financial Services, ed. Boosting your occupational pension. London: Financial Services Authority, 1999.

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Book chapters on the topic "Boosting"

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Forsyth, David. "Boosting." In Applied Machine Learning, 275–302. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-18114-7_12.

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Zhou, Zhi-Hua. "Boosting." In Encyclopedia of Database Systems, 1–4. New York, NY: Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4899-7993-3_568-2.

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Chu, Jianghao, Tae-Hwy Lee, Aman Ullah, and Ran Wang. "Boosting." In Macroeconomic Forecasting in the Era of Big Data, 431–63. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-31150-6_14.

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Zhou, Zhi-Hua. "Boosting." In Encyclopedia of Database Systems, 260–63. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-39940-9_568.

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Berk, Richard A. "Boosting." In Statistical Learning from a Regression Perspective, 259–89. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-44048-4_6.

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Munro, Paul, Hannu Toivonen, Geoffrey I. Webb, Wray Buntine, Peter Orbanz, Yee Whye Teh, Pascal Poupart, et al. "Boosting." In Encyclopedia of Machine Learning, 136–37. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_84.

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Williams, Graham. "Boosting." In Data Mining with Rattle and R, 269–91. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-9890-3_13.

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Li, Hang. "Boosting." In Machine Learning Methods, 179–99. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-3917-6_8.

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Schonlau, Matthias. "Boosting." In Applied Statistical Learning, 205–35. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-33390-3_11.

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Kanamori, Takafumi, Kohei Hatano, and Osamu Watanabe. "Boosting." In Computer Vision, 1–7. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-03243-2_836-1.

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Conference papers on the topic "Boosting"

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Warni, Elly, Dea Wahsa Saputri, A. Ais Prayogi Alimuddin, Muhammad Rizal H, Yunifa Miftachul Arif, and Irfan Tanzilur Rahman. "Boosting Methods Comparison : XGBoost, Adaboost and Gradient Boosting for Business Partner Performance Prediction." In 2024 8th International Conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE), 376–80. IEEE, 2024. http://dx.doi.org/10.1109/icitisee63424.2024.10730221.

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Paul, Indrani, Srilatha Manne, Manish Arora, W. Lloyd Bircher, and Sudhakar Yalamanchili. "Cooperative boosting." In the 40th Annual International Symposium. New York, New York, USA: ACM Press, 2013. http://dx.doi.org/10.1145/2485922.2485947.

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Xiang, Zhen James, and Peter J. Ramadge. "Sparse boosting." In ICASSP 2009 - 2009 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, 2009. http://dx.doi.org/10.1109/icassp.2009.4959911.

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Herlihy, Maurice, and Eric Koskinen. "Transactional boosting." In the 13th ACM SIGPLAN Symposium. New York, New York, USA: ACM Press, 2008. http://dx.doi.org/10.1145/1345206.1345237.

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Masnadi-Shirazi, Hamed, and Nuno Vasconcelos. "Asymmetric boosting." In the 24th international conference. New York, New York, USA: ACM Press, 2007. http://dx.doi.org/10.1145/1273496.1273573.

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Cochran, Robert A., Loris D'Antoni, Benjamin Livshits, David Molnar, and Margus Veanes. "Program Boosting." In POPL '15: The 42nd Annual ACM SIGPLAN-SIGACT Symposium on Principles of Programming Languages. New York, NY, USA: ACM, 2015. http://dx.doi.org/10.1145/2676726.2676973.

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Yan Jiang and Xiaoqing Ding. "Bhattacharyya boosting." In 2008 19th International Conference on Pattern Recognition (ICPR). IEEE, 2008. http://dx.doi.org/10.1109/icpr.2008.4761134.

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Liu, Yang, Zhuo Ma, Ximeng Liu, Siqi Ma, Surya Nepal, Robert H. Deng, and Kui Ren. "Boosting Privately: Federated Extreme Gradient Boosting for Mobile Crowdsensing." In 2020 IEEE 40th International Conference on Distributed Computing Systems (ICDCS). IEEE, 2020. http://dx.doi.org/10.1109/icdcs47774.2020.00017.

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Roberto, Marcello Augustus Ramos, Jean Carlos Dias E Silva, Herbert Prince Koelln, Alan Carlos Bernardes, Fabio Alves Albuquerque, Guilherme Miranda Paternost, Ana Margarida De Oliveira, Gilberto Magalhães Xavier, Jurandir Antônio Gomes Da Silva, and Otavio Cardoso Da Costa. "Boosting To Boosting: A New Approach To Enhance, Support And Maximize Subsea Processing And Boosting Applications." In Offshore Technology Conference. OTC, 2023. http://dx.doi.org/10.4043/32612-ms.

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Abstract Over the last 20 years subsea processing and boosting (P&B) technologies have supported and pushed forward the offshore oil and gas production, maximizing field production and recovery factor. Although those technologies achieved remarkable success, P&B is still not a heavy-weight business, despite Operators considering it adds great value. Therefore, to enhance and maximize P&B applications, a task force was created to identify the main hurdles, showstoppers and barriers, as well as to establish a robust process, methods and procedures related, including O&G Operators and Suppliers point of views, internal standards and international codes analysis. Afterwards, a critical analysis was made, more than ten initiatives were prioritized, and a Program named Bosting to Boosting (B2B) was structured. This paper will present all initiatives have been carried out in B2B and the main results for subsea projects.

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Oliaei, O. "Oversampled gain-boosting." In Proceedings of the International Symposium on Low Power Electronics and Design. IEEE, 2002. http://dx.doi.org/10.1109/lpe.2002.146742.

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Reports on the topic "Boosting"

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Skone, Timothy J. CO2 Pressure Boosting. Office of Scientific and Technical Information (OSTI), July 2012. http://dx.doi.org/10.2172/1509343.

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Skone, Timothy J. Gathering and boosting flaring. Office of Scientific and Technical Information (OSTI), January 2018. http://dx.doi.org/10.2172/1559822.

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Steve Arnold, Craig Balis, Pierre Barthelet, Etienne Poix, Tariq Samad, Greg Hampson, and S. M. Shahed. Garrett Electric Boosting Systems (EBS) Program. Office of Scientific and Technical Information (OSTI), March 2005. http://dx.doi.org/10.2172/910121.

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Water Management Institute, International. Boosting water benefits in West Bengal. International Water Management Institute (IWMI), 2012. http://dx.doi.org/10.5337/2012.004.

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Skone, Timothy J. Gathering and boosting centrifugal compression venting. Office of Scientific and Technical Information (OSTI), January 2018. http://dx.doi.org/10.2172/1559821.

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Water Management Institute, International. Improving soils and boosting yields in Thailand. International Water Management Institute (IWMI), 2010. http://dx.doi.org/10.5337/2011.0031.

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Skone, Timothy J. Gathering and boosting acid gas removal (AGR). Office of Scientific and Technical Information (OSTI), January 2018. http://dx.doi.org/10.2172/1559820.

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Markus, Maurer, Khammounty Bounseng, Morlok Michael, and Teutoburg-Weiss Hannes. Boosting Growth and Transformation in Laos’ Industry. Swiss National Science Foundation (SNSF), October 2019. http://dx.doi.org/10.46446/publication_r4d.2019.2.en.

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Two thirds of Laos’ manufacturing industry has experienced growth and transformation over the last half decade. There are noteworthy differences between small and large companies: whilst both grew quickly, larger ones have achieved more rapid change in products, technology and organisation. However, a dimension of growth and transformation where large companies in Laos are lagging behind is labour productivity.

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Zilberman, Mark. Shouldn’t Doppler 'De-boosting' be accounted for in calculations of intrinsic luminosity of Standard Candles? Intellectual Archive, September 2021. http://dx.doi.org/10.32370/iaj.2569.

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"Doppler boosting / de-boosting" is a well-known relativistic effect that alters the apparent luminosity of approaching/receding radiation sources. "Doppler boosting" alters the apparent luminosity of approaching light sources to appear brighter, while "Doppler de-boosting" alters the apparent luminosity of receding light sources to appear fainter. While "Doppler boosting / de-boosting" has been successfully accounted for and observed in relativistic jets of AGN, double white dwarfs, in search of exoplanets and stars in binary systems it was ignored in the establishment of Standard Candles for cosmological distances. A Standard Candle adjustment appears necessary for "Doppler de-boosting" for high Z, otherwise we would incorrectly assume that Standard Candles appear dimmer, not because of "Doppler de-boosting" but because of the excessive distance, which would affect the entire Standard Candles ladder at cosmological distances. The ratio between apparent (L) and intrinsic (Lo) luminosities as a function of redshift Z and spectral index α is given by the formula ℳ(Z) = L/Lo=(Z+1)^(α-3) and for Type Ia supernova as ℳ(Z) = L/Lo=(Z+1)^(-2). These formulas are obtained within the framework of Special Relativity and may require adjustments within the General Relativity framework.

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Zilberman, Mark. An Adjustment of the Apparent Luminosity of Standard Candles for the 'De-boosting' Effect. Intellectual Archive, February 2022. http://dx.doi.org/10.32370/iaj.2639.

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“De-boosting” is a well-known relativistic effect that alters the apparent luminosity of radiation sources with the non-zero redshift parameter z. It exists in both Special Relativity and General Relativity frameworks and is proportional to (z+1)^(-2). While the “boosting” (for blueshift) and “de-boosting” (for redshift) of light sources has been successfully accounted for and observed in research of various astronomical objects, it was ignored in the establishment of Standard candles for cosmological distances. A Standard candle adjustment appears necessary for “de-boosting” for high z, otherwise we would incorrectly assume that Standard candles appear dimmer, not because of “de-boosting” but because of the excessive distance, which would affect the entire Standard candles ladder at cosmological distances. The “de-boosting” correction of the apparent luminosities of SNIa places the effective rest-frame magnitudes below the curve corresponding to the cosmological model with the parameters Λ=0 and (ΩM, ΩΛ) = (2, 0) on the Hubble diagram. This way, the “de-boosting” correction of the apparent luminosities of SNIa may further adjust and clarify cosmological models.

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