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Artykuły w czasopismach na temat "Boosting"
Singh, Sandeep, i Guy W. Fried. "“Boosting”". Medicine & Science in Sports & Exercise 38, Supplement (maj 2006): S479. http://dx.doi.org/10.1249/00005768-200605001-02879.
Pełny tekst źródłaFearn, Tom. "Boosting". NIR news 18, nr 1 (luty 2007): 11–12. http://dx.doi.org/10.1255/nirn.1004.
Pełny tekst źródłaBühlmann, Peter, i Bin Yu. "Boosting". WIREs Computational Statistics 2, nr 1 (31.12.2009): 69–74. http://dx.doi.org/10.1002/wics.55.
Pełny tekst źródłaBecker, Thijs, Melvin Geubbelmans, Axel-Jan Rousseau, Dirk Valkenborg i Tomasz Burzykowski. "Boosting". American Journal of Orthodontics and Dentofacial Orthopedics 165, nr 1 (styczeń 2024): 122–24. http://dx.doi.org/10.1016/j.ajodo.2023.10.003.
Pełny tekst źródłaOnoda, Takashi. "Overfitting of boosting and regularized Boosting algorithms". Electronics and Communications in Japan (Part III: Fundamental Electronic Science) 90, nr 9 (2007): 69–78. http://dx.doi.org/10.1002/ecjc.20344.
Pełny tekst źródłaWojtys, Edward M. "Boosting Performance". Sports Health: A Multidisciplinary Approach 13, nr 2 (24.02.2021): 109–10. http://dx.doi.org/10.1177/1941738121991495.
Pełny tekst źródłaOtohwo, I. O., i D. R. Sadoh. "Boosting numbers". British Dental Journal 197, nr 8 (październik 2004): 449. http://dx.doi.org/10.1038/sj.bdj.4811778.
Pełny tekst źródłaLeigh-Smith, S. "Blood boosting". British Journal of Sports Medicine 38, nr 1 (1.02.2004): 99–101. http://dx.doi.org/10.1136/bjsm.2003.007195.
Pełny tekst źródłaPereira, M. "Boosting competitiveness". IEE Review 50, nr 5 (1.05.2004): 35–37. http://dx.doi.org/10.1049/ir:20040504.
Pełny tekst źródłaEllis, Andrew. "Boosting bandwidth". Physics World 29, nr 4 (kwiecień 2016): 17. http://dx.doi.org/10.1088/2058-7058/29/4/29.
Pełny tekst źródłaRozprawy doktorskie na temat "Boosting"
Lin, Wei-Chao. "Boosting image annotation". Thesis, University of Sunderland, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.512013.
Pełny tekst źródłaThompson, Simon Giles. "Distributed boosting algorithms". Thesis, University of Portsmouth, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.285529.
Pełny tekst źródłaZhou, Mian. "Gobor-boosting face recognition". Thesis, University of Reading, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.494814.
Pełny tekst źródłaANIBOLETE, 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.
Pełny tekst źródłaWith 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.
SALOMONI, MATTEO. "Boosting scintillation based detection". Doctoral thesis, Università degli Studi di Milano-Bicocca, 2019. http://hdl.handle.net/10281/241285.
Pełny tekst źródłaDuring 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.
Hofner, Benjamin. "Boosting in structured additive models". Diss., lmu, 2011. http://nbn-resolving.de/urn:nbn:de:bvb:19-138053.
Pełny tekst źródłaRätsch, Gunnar. "Robust boosting via convex optimization". Phd thesis, Universität Potsdam, 2001. http://opus.kobv.de/ubp/volltexte/2005/39/.
Pełny tekst źródłaDie 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.
Chan, Jeffrey (Jeffrey D. ). "On boosting and noisy labels". Thesis, Massachusetts Institute of Technology, 2015. http://hdl.handle.net/1721.1/100297.
Pełny tekst źródłaThis 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.
Bjurgert, Johan. "System Identification by Adaptive Boosting". Thesis, KTH, Reglerteknik, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-179711.
Pełny tekst źródłaInom 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.
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.
Pełny tekst źródłaKsiążki na temat "Boosting"
Maccaro, Janet C. Brain-boosting foods. Lake Mary, Fla: Siloam, 2008.
Znajdź pełny tekst źródłaAllen, Kelly-Ann, i Peggy Kern. Boosting School Belonging. Abingdon, Oxon ; New York, NY : Routledge, 2019.: Routledge, 2019. http://dx.doi.org/10.4324/9780203729632.
Pełny tekst źródłaWhite, Sandra Sardella. Boosting your energy. Redaktorzy Bennett Hilary, Gilbert Susan 1950-, Dawson, D. M. (David Michael), 1930-, Komaroff Anthony L, Dadoly Ann Marie i Harvard Medical School. Health Publications Group. Boston, MA: Harvard Health Publications, 2008.
Znajdź pełny tekst źródłaThompson, Simon Giles. Distributed boosting algorithms. Portsmouth: University of Portsmouth, 1999.
Znajdź pełny tekst źródłaSteer, Wilston. Boosting the Bay. North Bay, Ont: North Bay and District Chamber of Commerce, 1994.
Znajdź pełny tekst źródłaSatterfield, Jason M. Boosting your emotional intelligence. Chantilly, VA: Teaching Co., 2017.
Znajdź pełny tekst źródłaCamarinha-Matos, Luis M., Hamideh Afsarmanesh i Angel Ortiz, red. Boosting Collaborative Networks 4.0. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-62412-5.
Pełny tekst źródłaCogen, Victor. Boosting the Adolescent Underachiever. Boston, MA: Springer US, 1992. http://dx.doi.org/10.1007/978-1-4899-6576-9.
Pełny tekst źródłaSchapire, Robert E. Boosting: Foundations and algorithms. Cambridge, MA: MIT Press, 2012.
Znajdź pełny tekst źródłaVickery, Graham. Boosting businesses: Advisory services. Paris, France: Organisation for Economic Co-operation and Development, 1995.
Znajdź pełny tekst źródłaCzęści książek na temat "Boosting"
Forsyth, David. "Boosting". W Applied Machine Learning, 275–302. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-18114-7_12.
Pełny tekst źródłaZhou, Zhi-Hua. "Boosting". W 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.
Pełny tekst źródłaChu, Jianghao, Tae-Hwy Lee, Aman Ullah i Ran Wang. "Boosting". W 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.
Pełny tekst źródłaZhou, Zhi-Hua. "Boosting". W Encyclopedia of Database Systems, 260–63. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-39940-9_568.
Pełny tekst źródłaBerk, Richard A. "Boosting". W 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.
Pełny tekst źródłaMunro, Paul, Hannu Toivonen, Geoffrey I. Webb, Wray Buntine, Peter Orbanz, Yee Whye Teh, Pascal Poupart i in. "Boosting". W Encyclopedia of Machine Learning, 136–37. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_84.
Pełny tekst źródłaWilliams, Graham. "Boosting". W 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.
Pełny tekst źródłaLi, Hang. "Boosting". W Machine Learning Methods, 179–99. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-3917-6_8.
Pełny tekst źródłaKanamori, Takafumi, Kohei Hatano i Osamu Watanabe. "Boosting". W Computer Vision, 1–7. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-03243-2_836-1.
Pełny tekst źródłaBerk, Richard A. "Boosting". W Statistical Learning from a Regression Perspective, 297–337. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-40189-4_6.
Pełny tekst źródłaStreszczenia konferencji na temat "Boosting"
Paul, Indrani, Srilatha Manne, Manish Arora, W. Lloyd Bircher i Sudhakar Yalamanchili. "Cooperative boosting". W the 40th Annual International Symposium. New York, New York, USA: ACM Press, 2013. http://dx.doi.org/10.1145/2485922.2485947.
Pełny tekst źródłaXiang, Zhen James, i Peter J. Ramadge. "Sparse boosting". W ICASSP 2009 - 2009 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, 2009. http://dx.doi.org/10.1109/icassp.2009.4959911.
Pełny tekst źródłaHerlihy, Maurice, i Eric Koskinen. "Transactional boosting". W the 13th ACM SIGPLAN Symposium. New York, New York, USA: ACM Press, 2008. http://dx.doi.org/10.1145/1345206.1345237.
Pełny tekst źródłaMasnadi-Shirazi, Hamed, i Nuno Vasconcelos. "Asymmetric boosting". W the 24th international conference. New York, New York, USA: ACM Press, 2007. http://dx.doi.org/10.1145/1273496.1273573.
Pełny tekst źródłaCochran, Robert A., Loris D'Antoni, Benjamin Livshits, David Molnar i Margus Veanes. "Program Boosting". W 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.
Pełny tekst źródłaYan Jiang i Xiaoqing Ding. "Bhattacharyya boosting". W 2008 19th International Conference on Pattern Recognition (ICPR). IEEE, 2008. http://dx.doi.org/10.1109/icpr.2008.4761134.
Pełny tekst źródłaLiu, Yang, Zhuo Ma, Ximeng Liu, Siqi Ma, Surya Nepal, Robert H. Deng i Kui Ren. "Boosting Privately: Federated Extreme Gradient Boosting for Mobile Crowdsensing". W 2020 IEEE 40th International Conference on Distributed Computing Systems (ICDCS). IEEE, 2020. http://dx.doi.org/10.1109/icdcs47774.2020.00017.
Pełny tekst źródłaRoberto, 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 i Otavio Cardoso Da Costa. "Boosting To Boosting: A New Approach To Enhance, Support And Maximize Subsea Processing And Boosting Applications". W Offshore Technology Conference. OTC, 2023. http://dx.doi.org/10.4043/32612-ms.
Pełny tekst źródłaOliaei, O. "Oversampled gain-boosting". W Proceedings of the International Symposium on Low Power Electronics and Design. IEEE, 2002. http://dx.doi.org/10.1109/lpe.2002.146742.
Pełny tekst źródłaBarnhart, Stephen K. "Boosting Pasture Production". W Proceedings of the 16th Annual Integrated Crop Management Conference. Iowa State University, Digital Press, 2007. http://dx.doi.org/10.31274/icm-180809-875.
Pełny tekst źródłaRaporty organizacyjne na temat "Boosting"
Skone, Timothy J. CO2 Pressure Boosting. Office of Scientific and Technical Information (OSTI), lipiec 2012. http://dx.doi.org/10.2172/1509343.
Pełny tekst źródłaSkone, Timothy J. Gathering and boosting flaring. Office of Scientific and Technical Information (OSTI), styczeń 2018. http://dx.doi.org/10.2172/1559822.
Pełny tekst źródłaSteve Arnold, Craig Balis, Pierre Barthelet, Etienne Poix, Tariq Samad, Greg Hampson i S. M. Shahed. Garrett Electric Boosting Systems (EBS) Program. Office of Scientific and Technical Information (OSTI), marzec 2005. http://dx.doi.org/10.2172/910121.
Pełny tekst źródłaWater Management Institute, International. Boosting water benefits in West Bengal. International Water Management Institute (IWMI), 2012. http://dx.doi.org/10.5337/2012.004.
Pełny tekst źródłaSkone, Timothy J. Gathering and boosting centrifugal compression venting. Office of Scientific and Technical Information (OSTI), styczeń 2018. http://dx.doi.org/10.2172/1559821.
Pełny tekst źródłaWater Management Institute, International. Improving soils and boosting yields in Thailand. International Water Management Institute (IWMI), 2010. http://dx.doi.org/10.5337/2011.0031.
Pełny tekst źródłaSkone, Timothy J. Gathering and boosting acid gas removal (AGR). Office of Scientific and Technical Information (OSTI), styczeń 2018. http://dx.doi.org/10.2172/1559820.
Pełny tekst źródłaMarkus, Maurer, Khammounty Bounseng, Morlok Michael i Teutoburg-Weiss Hannes. Boosting Growth and Transformation in Laos’ Industry. Swiss National Science Foundation (SNSF), październik 2019. http://dx.doi.org/10.46446/publication_r4d.2019.2.en.
Pełny tekst źródłaZilberman, Mark. Shouldn’t Doppler 'De-boosting' be accounted for in calculations of intrinsic luminosity of Standard Candles? Intellectual Archive, wrzesień 2021. http://dx.doi.org/10.32370/iaj.2569.
Pełny tekst źródłaArnold, Steve, Craig Balis, Pierre Barthelet, Etienne Poix, Tariq Samad, Greg Hampson i S. M. Shahed. Electric Boosting System for Light Truck/SUV Application. Office of Scientific and Technical Information (OSTI), czerwiec 2005. http://dx.doi.org/10.2172/841240.
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