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Journal articles on the topic 'Boosting'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

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

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2

Fearn, Tom. "Boosting." NIR news 18, no. 1 (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 (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 (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 (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 (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 (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 (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 (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 (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 (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 (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 (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 (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 (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 (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 (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 (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 (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

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22

Tucker, H. "Boot Boosting." ITNOW 53, no. 1 (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 (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 (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 (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. Becaus

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26

Fanning, Paul. "Boosting Confidence." Manufacturing Management 2024, no. 1-2 (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, et al. "Ketoconazole and Lopinavir/Ritonavir Coadministration: Boosting beyond Boosting." AIDS Research and Human Retroviruses 19, no. 10 (2003): 941–42. http://dx.doi.org/10.1089/088922203322493148.

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28

Sah, Andrian, Chaeroen Niesa, Rhaishudin Rumandan Jafar, and Muhammad Muharrom. "Analisis Model Prediksi Penyakit Jantung Menggunakan Adaptive Boosting, Gradient Boosting, dan Extreme Gradient Boosting." Jurnal Ilmiah FIFO 17, no. 1 (2025): 46. https://doi.org/10.22441/fifo.2025.v17i1.006.

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Deteksi dini penyakit jantung merupakan langkah penting untuk meningkatkan kualitas diagnosis dan perawatan pasien. Namun, metode prediksi manual yang sering digunakan tenaga medis memiliki keterbatasan dalam efisiensi waktu, akurasi, dan kemampuan menangani volume data yang besar. Dalam bidang kecerdasan buatan, algoritma machine learning seperti Adaptive Boosting (AdaBoost), Gradient Boosting, dan Extreme Gradient Boosting (XGBoost) menawarkan potensi untuk meningkatkan akurasi prediksi, terutama dalam mengatasi tantangan pada dataset kecil yang sering mengalami ketidakseimbangan kelas dan r

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29

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 (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 p

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30

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 en

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31

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

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32

Sipper, Moshe, and Jason H. Moore. "Symbolic-regression boosting." Genetic Programming and Evolvable Machines 22, no. 3 (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 (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 (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

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35

Melamies, Inès A. "Quality boosting plasma." IST International Surface Technology 10, no. 3 (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 (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 (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 (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 (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 bo

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42

Schirmer, Barbara R. "Boosting Reading Success." TEACHING Exceptional Children 30, no. 1 (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 (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 (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 (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 (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 (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 (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 (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 (2006): 287–311. http://dx.doi.org/10.1198/106186006x113548.

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