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Artículos de revistas sobre el tema "Boosting"

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Autor: Grafiati
Publicado: 4 de junio de 2021
Última modificación: 25 de mayo de 2024

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1

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

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2

Fearn, Tom. "Boosting". NIR news 18, n.º 1 (febrero de 2007): 11–12. http://dx.doi.org/10.1255/nirn.1004.

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3

Bühlmann, Peter y Bin Yu. "Boosting". WIREs Computational Statistics 2, n.º 1 (31 de diciembre de 2009): 69–74. http://dx.doi.org/10.1002/wics.55.

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4

Becker, Thijs, Melvin Geubbelmans, Axel-Jan Rousseau, Dirk Valkenborg y Tomasz Burzykowski. "Boosting". American Journal of Orthodontics and Dentofacial Orthopedics 165, n.º 1 (enero de 2024): 122–24. http://dx.doi.org/10.1016/j.ajodo.2023.10.003.

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5

Onoda, Takashi. "Overfitting of boosting and regularized Boosting algorithms". Electronics and Communications in Japan (Part III: Fundamental Electronic Science) 90, n.º 9 (2007): 69–78. http://dx.doi.org/10.1002/ecjc.20344.

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6

Wojtys, Edward M. "Boosting Performance". Sports Health: A Multidisciplinary Approach 13, n.º 2 (24 de febrero de 2021): 109–10. http://dx.doi.org/10.1177/1941738121991495.

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7

Otohwo, I. O. y D. R. Sadoh. "Boosting numbers". British Dental Journal 197, n.º 8 (octubre de 2004): 449. http://dx.doi.org/10.1038/sj.bdj.4811778.

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8

Leigh-Smith, S. "Blood boosting". British Journal of Sports Medicine 38, n.º 1 (1 de febrero de 2004): 99–101. http://dx.doi.org/10.1136/bjsm.2003.007195.

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9

Pereira, M. "Boosting competitiveness". IEE Review 50, n.º 5 (1 de mayo de 2004): 35–37. http://dx.doi.org/10.1049/ir:20040504.

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10

Ellis, Andrew. "Boosting bandwidth". Physics World 29, n.º 4 (abril de 2016): 17. http://dx.doi.org/10.1088/2058-7058/29/4/29.

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11

Phelps, J., E. L. Webb, D. Bickford, V. Nijman y N. S. Sodhi. "Boosting CITES". Science 330, n.º 6012 (23 de diciembre de 2010): 1752–53. http://dx.doi.org/10.1126/science.1195558.

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12

T. M. B. "Boosting Fusion". Scientific American 263, n.º 3 (septiembre de 1990): 30–31. http://dx.doi.org/10.1038/scientificamerican0990-30a.

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13

Cochran, Robert A., Loris D'Antoni, Benjamin Livshits, David Molnar y Margus Veanes. "Program Boosting". ACM SIGPLAN Notices 50, n.º 1 (11 de mayo de 2015): 677–88. http://dx.doi.org/10.1145/2775051.2676973.

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14

Lewis, Sian. "Boosting regeneration". Nature Reviews Neuroscience 19, n.º 12 (22 de octubre de 2018): 713. http://dx.doi.org/10.1038/s41583-018-0083-3.

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15

Hines, P. J. "Boosting Biofuels". Science 331, n.º 6013 (6 de enero de 2011): 11. http://dx.doi.org/10.1126/science.331.6013.11-a.

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16

Powell, Kendall. "Boosting business". Nature 516, n.º 7529 (diciembre de 2014): 133–35. http://dx.doi.org/10.1038/nj7529-133a.

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17

Holzfuss, Joachim, Matthias Rüggeberg y Robert Mettin. "Boosting Sonoluminescence". Physical Review Letters 81, n.º 9 (31 de agosto de 1998): 1961–64. http://dx.doi.org/10.1103/physrevlett.81.1961.

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18

Van de Vyver, Julie, Diane M. Houston, Dominic Abrams y Milica Vasiljevic. "Boosting Belligerence". Psychological Science 27, n.º 2 (16 de diciembre de 2015): 169–77. http://dx.doi.org/10.1177/0956797615615584.

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19

HESS, GLENN. "BOOSTING GENERICS". Chemical & Engineering News 85, n.º 10 (5 de marzo de 2007): 48–49. http://dx.doi.org/10.1021/cen-v085n010.p048.

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20

Pham, Thang V. y Arnold W. M. Smeulders. "Quadratic boosting". Pattern Recognition 41, n.º 1 (enero de 2008): 331–41. http://dx.doi.org/10.1016/j.patcog.2007.05.008.

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21

Agrafiotis, Dimitris K., Alan Gibbs, Fangqiang Zhu, Sergei Izrailev y Eric Martin. "Conformational Boosting". Australian Journal of Chemistry 59, n.º 12 (2006): 874. http://dx.doi.org/10.1071/ch06217.

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Stochastic proximity embedding (SPE) is a novel self-organizing algorithm for sampling conformational space using geometric constraints derived from the molecular connectivity table. Here, we describe a simple heuristic that can be used in conjunction with SPE to bias the conformational search towards more extended or compact conformations, and thus greatly expand the range of geometries sampled during the search. The method uses a boosting strategy to generate a series of conformations, each of which is at least as extended (or compact) as the previous one. The approach is compared to several popular conformational sampling techniques using a reference set of 59 bioactive ligands extracted from the Protein Data Bank, and is shown to be significantly more effective in sampling the full range of molecular radii, with the exception of the Catalyst program, which was equally effective.
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22

Tucker, H. "Boot Boosting". ITNOW 53, n.º 1 (23 de diciembre de 2010): 16–17. http://dx.doi.org/10.1093/itnow/bwq227.

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23

Parmley, Stephen. "Boosting adjuvants". Science-Business eXchange 7, n.º 44 (noviembre de 2014): 1281. http://dx.doi.org/10.1038/scibx.2014.1281.

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24

Paul, Indrani, Srilatha Manne, Manish Arora, W. Lloyd Bircher y Sudhakar Yalamanchili. "Cooperative boosting". ACM SIGARCH Computer Architecture News 41, n.º 3 (26 de junio de 2013): 285–96. http://dx.doi.org/10.1145/2508148.2485947.

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25

Fanning, Paul. "Boosting Confidence". Manufacturing Management 2024, n.º 1-2 (enero de 2024): 18–19. http://dx.doi.org/10.12968/s2514-9768(24)90027-5.

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26

Bouchetob, Elaid y Bouchra Nadji. "Boosting Reliability". International journal of electrical and computer engineering systems 15, n.º 4 (28 de marzo de 2024): 313–20. http://dx.doi.org/10.32985/ijeces.15.4.2.

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Reliability is very important in the world of electronic device design and production, particularly in applications where continuous and flawless performance is a necessity. This directs our attention to the boost converter, which forms the foundation of power electronics, renewable energy systems, and electric vehicles. However, as technology progresses, the choice of materials for these converters is a big challenge. For that, in this paper, the impact of using Silicon Carbide (SiC) devices, with their promising material properties, on the reliability of boost converters is presented. Because the results showed that more than 80% of boost converter failures are caused by semiconductors, the use of SiC materials is assessed by determining its reliability using MIL-HDBK-217 standard. In addition, a comparative study with the use of traditional Silicon (Si) is conducted. The results showed that the failure rate of boost converters based on SiC devices reduced from 8.335 failure/10-6h to 6.243 failure/10-6h. This notable shift in failure rates establishes SiC as a pivotal material in the evolution of boost converter technology, offering a compelling solution to address the persistent challenges associated with semiconductor-related failures.
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27

Boffito, Marta, Stefano Bonora, Paola Sales, Ivano Dal Conte, Alessandro Sinicco, Patrick G. Hoggard, Saye Khoo, David J. Back y Giovanni Di Perri. "Ketoconazole and Lopinavir/Ritonavir Coadministration: Boosting beyond Boosting". AIDS Research and Human Retroviruses 19, n.º 10 (octubre de 2003): 941–42. http://dx.doi.org/10.1089/088922203322493148.

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28

Sun, Xiao Wei y Hong Bo Zhou. "An Empirical Evaluation of Boosting-BAN and Boosting-MultiTAN". Applied Mechanics and Materials 513-517 (febrero de 2014): 506–9. http://dx.doi.org/10.4028/www.scientific.net/amm.513-517.506.

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An ensemble consists of a set of independently trained classifiers whose predictions are combined when classifying novel instances. Previous research has shown that an ensemble as a whole is often more accurate than any of the single classifiers in the ensemble. Boosting-BAN classifier is considered stronger than Boosting-MultiTAN on noise-free data. However, there are strong empirical indications that Boosting-MultiTAN is much more robust than Boosting-BAN in noisy settings. For this reason, in this paper we built an ensemble using a voting methodology of Boosting-BAN and Boosting-MultiTAN ensembles with 10 sub-classifiers in each one. We performed a comparison with Boosting-BAN and Boosting-MultiTAN ensembles with 25 sub-classifiers on standard benchmark datasets and the proposed technique was the most accurate.
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29

Fangjun Wu. "Comparing Boosting and Cost-Sensitive Boosting With Imbalanced Data". Journal of Convergence Information Technology 7, n.º 21 (30 de noviembre de 2012): 1–8. http://dx.doi.org/10.4156/jcit.vol7.issue21.1.

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30

Sipper, Moshe y Jason H. Moore. "Symbolic-regression boosting". Genetic Programming and Evolvable Machines 22, n.º 3 (23 de marzo de 2021): 357–81. http://dx.doi.org/10.1007/s10710-021-09400-0.

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31

Ghani, Osman. "Boosting Renewable Energy". CFA Institute Magazine 24, n.º 2 (marzo de 2013): 16–17. http://dx.doi.org/10.2469/cfm.v24.n2.5.

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32

Nikravan, Mohammad Hossein, Marjan Movahedan y Sandra Zilles. "Precision-based Boosting". Proceedings of the AAAI Conference on Artificial Intelligence 35, n.º 10 (18 de mayo de 2021): 9153–60. http://dx.doi.org/10.1609/aaai.v35i10.17105.

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AdaBoost is a highly popular ensemble classification method for which many variants have been published. This paper proposes a generic refinement of all of these AdaBoost variants. Instead of assigning weights based on the total error of the base classifiers (as in AdaBoost), our method uses class-specific error rates. On instance x it assigns a higher weight to a classifier predicting label y on x, if that classifier is less likely to make a mistake when it predicts class y. Like AdaBoost, our method is guaranteed to boost weak learners into strong learners. An empirical study on AdaBoost and one of its multi-class versions, SAMME, demonstrates the superiority of our method on datasets with more than 1,000 instances as well as on datasets with more than three classes.
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33

Melamies, Inès A. "Quality boosting plasma". IST International Surface Technology 10, n.º 3 (noviembre de 2017): 54–55. http://dx.doi.org/10.1007/s35724-017-0049-4.

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34

Paiva, Stacey-Lynn. "Boosting solid stability". Nature Reviews Chemistry 6, n.º 3 (28 de febrero de 2022): 167. http://dx.doi.org/10.1038/s41570-022-00375-9.

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35

Nguyen, Hung V., H. Davulcu y V. Ramchandran. "Boosting Item Findability". International Journal of Intelligent Information Technologies 2, n.º 3 (julio de 2006): 1–20. http://dx.doi.org/10.4018/jiit.2006070101.

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36

Ricupito, Alessia, Matteo Grioni, Arianna Calcinotto y Matteo Bellone. "Boosting anticancer vaccines". OncoImmunology 2, n.º 7 (julio de 2013): e25032. http://dx.doi.org/10.4161/onci.25032.

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37

Joachimi, B. y P. Schneider. "Intrinsic alignment boosting". Astronomy and Astrophysics 517 (julio de 2010): A4. http://dx.doi.org/10.1051/0004-6361/201014482.

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38

Hannigan, C. "Boosting leadership skills". Veterinary Record 167, n.º 21 (20 de noviembre de 2010): i. http://dx.doi.org/10.1136/vr.g7057.

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39

Binder, H., O. Gefeller, M. Schmid y A. Mayr. "Extending Statistical Boosting". Methods of Information in Medicine 53, n.º 06 (2014): 428–35. http://dx.doi.org/10.3414/me13-01-0123.

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SummaryBackground: Boosting algorithms to simultaneously estimate and select predictor effects in statistical models have gained substantial interest during the last decade.Objectives: This review highlights recent methodological developments regarding boosting algorithms for statistical modelling especially focusing on topics relevant for biomedical research.Methods: We suggest a unified framework for gradient boosting and likelihood-based boosting (statistical boosting) which have been addressed separately in the literature up to now.Results: The methodological developments on statistical boosting during the last ten years can be grouped into three different lines of research: i) efforts to ensure variable selection leading to sparser models, ii) developments regarding different types of predictor effects and how to choose them, iii) approaches to extend the statistical boosting framework to new regression settings.Conclusions: Statistical boosting algorithms have been adapted to carry out unbiased variable selection and automated model choice during the fitting process and can nowadays be applied in almost any regression setting in combination with a large amount of different types of predictor effects.
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40

Schirmer, Barbara R. "Boosting Reading Success". TEACHING Exceptional Children 30, n.º 1 (septiembre de 1997): 52–55. http://dx.doi.org/10.1177/004005999703000110.

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41

Day, Michael A. "Boosting Student Vocabulary". Physics Teacher 57, n.º 2 (febrero de 2019): 91–93. http://dx.doi.org/10.1119/1.5088468.

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42

Garçon, Nathalie y Michel Goldman. "Boosting Vaccine Power". Scientific American 301, n.º 4 (octubre de 2009): 72–79. http://dx.doi.org/10.1038/scientificamerican1009-72.

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43

Scholes, Gregory D. y Edward H. Sargent. "Boosting plant biology". Nature Materials 13, n.º 4 (21 de marzo de 2014): 329–31. http://dx.doi.org/10.1038/nmat3926.

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44

Lin, Hai-Qing. "Boosting computational capabilities". Nature Materials 15, n.º 7 (22 de junio de 2016): 693–94. http://dx.doi.org/10.1038/nmat4675.

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45

Gérard, J. M. "Boosting photon storage". Nature Materials 2, n.º 3 (marzo de 2003): 140–41. http://dx.doi.org/10.1038/nmat847.

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46

Lee, Wendy. "Boosting speaking skills". Primary Teacher Update 2012, n.º 10 (julio de 2012): 22–24. http://dx.doi.org/10.12968/prtu.2012.1.10.22.

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47

Bühlmann, Peter y Bin Yu. "Boosting With theL2Loss". Journal of the American Statistical Association 98, n.º 462 (junio de 2003): 324–39. http://dx.doi.org/10.1198/016214503000125.

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48

Lutz, Roman Werner y Peter Bühlmann. "Conjugate Direction Boosting". Journal of Computational and Graphical Statistics 15, n.º 2 (junio de 2006): 287–311. http://dx.doi.org/10.1198/106186006x113548.

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49

RIDGE, RICHARD A. "Boosting insulin safety". Nursing 37, n.º 2 (febrero de 2007): 14–15. http://dx.doi.org/10.1097/00152193-200702000-00011.

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50

Masnadi-Shirazi, H. y N. Vasconcelos. "Cost-Sensitive Boosting". IEEE Transactions on Pattern Analysis and Machine Intelligence 33, n.º 2 (febrero de 2011): 294–309. http://dx.doi.org/10.1109/tpami.2010.71.

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