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Journal articles on the topic 'Boosting'

Author: Grafiati

Published: 4 June 2021

Last updated: 9 June 2025

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1

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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2

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

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3

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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4

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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5

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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6

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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7

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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8

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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9

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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10

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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11

Phelps, J., E. L. Webb, D. Bickford, V. Nijman, and N. S. Sodhi. "Boosting CITES." Science 330, no. 6012 (December 23, 2010): 1752–53. http://dx.doi.org/10.1126/science.1195558.

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12

T. M. B. "Boosting Fusion." Scientific American 263, no. 3 (September 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, and Margus Veanes. "Program Boosting." ACM SIGPLAN Notices 50, no. 1 (May 11, 2015): 677–88. http://dx.doi.org/10.1145/2775051.2676973.

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14

Lewis, Sian. "Boosting regeneration." Nature Reviews Neuroscience 19, no. 12 (October 22, 2018): 713. http://dx.doi.org/10.1038/s41583-018-0083-3.

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15

Hines, P. J. "Boosting Biofuels." Science 331, no. 6013 (January 6, 2011): 11. http://dx.doi.org/10.1126/science.331.6013.11-a.

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16

Powell, Kendall. "Boosting business." Nature 516, no. 7529 (December 2014): 133–35. http://dx.doi.org/10.1038/nj7529-133a.

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17

Holzfuss, Joachim, Matthias Rüggeberg, and Robert Mettin. "Boosting Sonoluminescence." Physical Review Letters 81, no. 9 (August 31, 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, and Milica Vasiljevic. "Boosting Belligerence." Psychological Science 27, no. 2 (December 16, 2015): 169–77. http://dx.doi.org/10.1177/0956797615615584.

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19

HESS, GLENN. "BOOSTING GENERICS." Chemical & Engineering News 85, no. 10 (March 5, 2007): 48–49. http://dx.doi.org/10.1021/cen-v085n010.p048.

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20

Pham, Thang V., and Arnold W. M. Smeulders. "Quadratic boosting." Pattern Recognition 41, no. 1 (January 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, and Eric Martin. "Conformational Boosting." Australian Journal of Chemistry 59, no. 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, no. 1 (December 23, 2010): 16–17. http://dx.doi.org/10.1093/itnow/bwq227.

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23

Parmley, Stephen. "Boosting adjuvants." Science-Business eXchange 7, no. 44 (November 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, and Sudhakar Yalamanchili. "Cooperative boosting." ACM SIGARCH Computer Architecture News 41, no. 3 (June 26, 2013): 285–96. http://dx.doi.org/10.1145/2508148.2485947.

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25

Bouchetob, Elaid, and Bouchra Nadji. "Boosting Reliability." International journal of electrical and computer engineering systems 15, no. 4 (March 28, 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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26

Fanning, Paul. "Boosting Confidence." Manufacturing Management 2024, no. 1-2 (January 2024): 18–19. http://dx.doi.org/10.12968/s2514-9768(24)90027-5.

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27

Boffito, Marta, Stefano Bonora, Paola Sales, Ivano Dal Conte, Alessandro Sinicco, Patrick G. Hoggard, Saye Khoo, David J. Back, and Giovanni Di Perri. "Ketoconazole and Lopinavir/Ritonavir Coadministration: Boosting beyond Boosting." AIDS Research and Human Retroviruses 19, no. 10 (October 2003): 941–42. http://dx.doi.org/10.1089/088922203322493148.

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28

Ramamoorthy, Kishorwara, Devika S. Pillai, Pradeep Kumar Yadalam, and Prasanthi Sitaraman. "Comparing Gradient Boosting and Neural Networks in Predicting Age Based on Coronal Pulp Height from Panoramic Radiographs – A Retrospective Radiographic Study." Journal of Indian Academy of Oral Medicine and Radiology 37, no. 1 (January 2025): 103–8. https://doi.org/10.4103/jiaomr.jiaomr_332_24.

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Background: Age estimation is the process of establishing an individual’s age using biological indicators. It is extremely important in many domains, including forensic science, anthropology, and legal medicine. Objectives: To compare the predictive accuracy and efficacy of gradient boosting and neural network models in estimating chronological age from coronal pulp height measurements using panoramic radiographs. Methods: Digital dental panoramic radiographs were obtained from institutional databases. The age estimation was done using the first molar as the reference tooth manually. For the prediction of age based on tooth coronal index (TCI) values, two machine learning models were developed: gradient boosting and neural networks. Hyperparameter tuning was performed to optimize the model’s performance, ensuring that it could accurately predict age from the TCI values. The models were tested for sensitivity and specificity. Results: The area under curve (AUC) values for neural network and gradient boosting are 0.821 and 0.959, respectively. Gradient boosting’s AUC of 0.959 indicates near-flawless classification ability, whereas the neural network’s 0.821 points to weaker performance. Gradient boosting has a classification accuracy of 0.765, significantly higher than the neural network’s 0.529, showing that gradient boosting makes fewer prediction errors. Conclusion: Gradient boosting excels in interpretability and efficiency with smaller datasets, in generalization. In contrast, neural networks are capable of modeling complex relationships within high-dimensional data but may require more resources and training for optimal performance.

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29

Sun, Xiao Wei, and Hong Bo Zhou. "An Empirical Evaluation of Boosting-BAN and Boosting-MultiTAN." Applied Mechanics and Materials 513-517 (February 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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30

Fangjun Wu. "Comparing Boosting and Cost-Sensitive Boosting With Imbalanced Data." Journal of Convergence Information Technology 7, no. 21 (November 30, 2012): 1–8. http://dx.doi.org/10.4156/jcit.vol7.issue21.1.

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31

Boginskaya, O. A. "BOOSTING IN DISSENTING OPINIONS: TYPES AND LEXICAL REALIZATIONS." Voprosy Kognitivnoy Lingvistiki, no. 3 (2023): 106–11. http://dx.doi.org/10.20916/1812-3228-2023-3-106-111.

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The paper explores the role of boosting as a metadiscourse feature used by justices in dissenting opinions to challenge the majority decisions and convince an audience. The aim is to identify the types of boosting and lexical items used for indicating certainty and commitment. As the study aims to analyze how boosting is realized linguistically, the methods of quantitative and interpretative analysis were applied. The study revealed that the justices make extensive use of boosters to show disagreement and persuade the audience to agree with their views. For this purpose, they use four types of boosting, including certainty boosters, intensity boosters, solidarity boosters, and extremity boosters with a quantitative predominance of the first type.

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32

Sipper, Moshe, and Jason H. Moore. "Symbolic-regression boosting." Genetic Programming and Evolvable Machines 22, no. 3 (March 23, 2021): 357–81. http://dx.doi.org/10.1007/s10710-021-09400-0.

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33

Ghani, Osman. "Boosting Renewable Energy." CFA Institute Magazine 24, no. 2 (March 2013): 16–17. http://dx.doi.org/10.2469/cfm.v24.n2.5.

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34

Nikravan, Mohammad Hossein, Marjan Movahedan, and Sandra Zilles. "Precision-based Boosting." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 10 (May 18, 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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35

Melamies, Inès A. "Quality boosting plasma." IST International Surface Technology 10, no. 3 (November 2017): 54–55. http://dx.doi.org/10.1007/s35724-017-0049-4.

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36

Paiva, Stacey-Lynn. "Boosting solid stability." Nature Reviews Chemistry 6, no. 3 (February 28, 2022): 167. http://dx.doi.org/10.1038/s41570-022-00375-9.

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37

Nguyen, Hung V., H. Davulcu, and V. Ramchandran. "Boosting Item Findability." International Journal of Intelligent Information Technologies 2, no. 3 (July 2006): 1–20. http://dx.doi.org/10.4018/jiit.2006070101.

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38

Ricupito, Alessia, Matteo Grioni, Arianna Calcinotto, and Matteo Bellone. "Boosting anticancer vaccines." OncoImmunology 2, no. 7 (July 2013): e25032. http://dx.doi.org/10.4161/onci.25032.

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39

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

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40

Hannigan, C. "Boosting leadership skills." Veterinary Record 167, no. 21 (November 20, 2010): i. http://dx.doi.org/10.1136/vr.g7057.

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41

Binder, H., O. Gefeller, M. Schmid, and A. Mayr. "Extending Statistical Boosting." Methods of Information in Medicine 53, no. 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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42

Schirmer, Barbara R. "Boosting Reading Success." TEACHING Exceptional Children 30, no. 1 (September 1997): 52–55. http://dx.doi.org/10.1177/004005999703000110.

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43

Day, Michael A. "Boosting Student Vocabulary." Physics Teacher 57, no. 2 (February 2019): 91–93. http://dx.doi.org/10.1119/1.5088468.

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44

Garçon, Nathalie, and Michel Goldman. "Boosting Vaccine Power." Scientific American 301, no. 4 (October 2009): 72–79. http://dx.doi.org/10.1038/scientificamerican1009-72.

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45

Scholes, Gregory D., and Edward H. Sargent. "Boosting plant biology." Nature Materials 13, no. 4 (March 21, 2014): 329–31. http://dx.doi.org/10.1038/nmat3926.

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46

Lin, Hai-Qing. "Boosting computational capabilities." Nature Materials 15, no. 7 (June 22, 2016): 693–94. http://dx.doi.org/10.1038/nmat4675.

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47

Gérard, J. M. "Boosting photon storage." Nature Materials 2, no. 3 (March 2003): 140–41. http://dx.doi.org/10.1038/nmat847.

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48

Lee, Wendy. "Boosting speaking skills." Primary Teacher Update 2012, no. 10 (July 2012): 22–24. http://dx.doi.org/10.12968/prtu.2012.1.10.22.

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49

Bühlmann, Peter, and Bin Yu. "Boosting With theL2Loss." Journal of the American Statistical Association 98, no. 462 (June 2003): 324–39. http://dx.doi.org/10.1198/016214503000125.

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50

Lutz, Roman Werner, and Peter Bühlmann. "Conjugate Direction Boosting." Journal of Computational and Graphical Statistics 15, no. 2 (June 2006): 287–311. http://dx.doi.org/10.1198/106186006x113548.

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