Artículos de revistas sobre el tema "Bayesian Machine Learning (BML)"
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Rigueira, Xurxo, María Pazo, María Araújo, Saki Gerassis y Elvira Bocos. "Bayesian Machine Learning and Functional Data Analysis as a Two-Fold Approach for the Study of Acid Mine Drainage Events". Water 15, n.º 8 (15 de abril de 2023): 1553. http://dx.doi.org/10.3390/w15081553.
Texto completoMobiny, Aryan, Aditi Singh y Hien Van Nguyen. "Risk-Aware Machine Learning Classifier for Skin Lesion Diagnosis". Journal of Clinical Medicine 8, n.º 8 (17 de agosto de 2019): 1241. http://dx.doi.org/10.3390/jcm8081241.
Texto completoOladyshkin, Sergey, Farid Mohammadi, Ilja Kroeker y Wolfgang Nowak. "Bayesian3 Active Learning for the Gaussian Process Emulator Using Information Theory". Entropy 22, n.º 8 (13 de agosto de 2020): 890. http://dx.doi.org/10.3390/e22080890.
Texto completoZhou, Ting, Xiaohu Wen, Qi Feng, Haijiao Yu y Haiyang Xi. "Bayesian Model Averaging Ensemble Approach for Multi-Time-Ahead Groundwater Level Prediction Combining the GRACE, GLEAM, and GLDAS Data in Arid Areas". Remote Sensing 15, n.º 1 (29 de diciembre de 2022): 188. http://dx.doi.org/10.3390/rs15010188.
Texto completoKim, Sungwon, Meysam Alizamir, Nam Won Kim y Ozgur Kisi. "Bayesian Model Averaging: A Unique Model Enhancing Forecasting Accuracy for Daily Streamflow Based on Different Antecedent Time Series". Sustainability 12, n.º 22 (21 de noviembre de 2020): 9720. http://dx.doi.org/10.3390/su12229720.
Texto completoNajafi, Mohammad Reza, Zahra Kavianpour, Banafsheh Najafi, Mohammad Reza Kavianpour y Hamid Moradkhani. "Air demand in gated tunnels – a Bayesian approach to merge various predictions". Journal of Hydroinformatics 14, n.º 1 (23 de abril de 2011): 152–66. http://dx.doi.org/10.2166/hydro.2011.108.
Texto completoXu, Ren, Nengcheng Chen, Yumin Chen y Zeqiang Chen. "Downscaling and Projection of Multi-CMIP5 Precipitation Using Machine Learning Methods in the Upper Han River Basin". Advances in Meteorology 2020 (9 de marzo de 2020): 1–17. http://dx.doi.org/10.1155/2020/8680436.
Texto completoShu, Meiyan, Shuaipeng Fei, Bingyu Zhang, Xiaohong Yang, Yan Guo, Baoguo Li y Yuntao Ma. "Application of UAV Multisensor Data and Ensemble Approach for High-Throughput Estimation of Maize Phenotyping Traits". Plant Phenomics 2022 (28 de agosto de 2022): 1–17. http://dx.doi.org/10.34133/2022/9802585.
Texto completoQuadeer, Ahmed A., Matthew R. McKay, John P. Barton y Raymond H. Y. Louie. "MPF–BML: a standalone GUI-based package for maximum entropy model inference". Bioinformatics 36, n.º 7 (18 de diciembre de 2019): 2278–79. http://dx.doi.org/10.1093/bioinformatics/btz925.
Texto completoSoria-Olivas, E., J. Gomez-Sanchis, J. D. Martin, J. Vila-Frances, M. Martinez, J. R. Magdalena y A. J. Serrano. "BELM: Bayesian Extreme Learning Machine". IEEE Transactions on Neural Networks 22, n.º 3 (marzo de 2011): 505–9. http://dx.doi.org/10.1109/tnn.2010.2103956.
Texto completoBiletskyy, B. "Distributed Bayesian Machine Learning Procedures". Cybernetics and Systems Analysis 55, n.º 3 (mayo de 2019): 456–61. http://dx.doi.org/10.1007/s10559-019-00153-4.
Texto completoChen, Yarui, Jucheng Yang, Chao Wang y DongSun Park. "Variational Bayesian extreme learning machine". Neural Computing and Applications 27, n.º 1 (24 de septiembre de 2014): 185–96. http://dx.doi.org/10.1007/s00521-014-1710-1.
Texto completoSuyama, Atsushi. "Introduction to Bayesian Machine Learning". Journal of the Robotics Society of Japan 40, n.º 10 (2022): 857–62. http://dx.doi.org/10.7210/jrsj.40.857.
Texto completoLi, Yifen, Yun Wang, Zhiya Chen y Runmin Zou. "Bayesian robust multi-extreme learning machine". Knowledge-Based Systems 210 (diciembre de 2020): 106468. http://dx.doi.org/10.1016/j.knosys.2020.106468.
Texto completoGandhi, Shipra, Sarabjot Pabla, Mary Nesline, Manu Pandey, Marc S. Ernstoff, Grace K. Dy, Jeffery M. Conroy et al. "Algorithmic prediction of response to checkpoint inhibitors: Hyperprogressors versus responders." Journal of Clinical Oncology 35, n.º 15_suppl (20 de mayo de 2017): 11565. http://dx.doi.org/10.1200/jco.2017.35.15_suppl.11565.
Texto completoWang, Peipei, Xinqi Zheng, Junhua Ku y Chunning Wang. "Multiple-Instance Learning Approach via Bayesian Extreme Learning Machine". IEEE Access 8 (2020): 62458–70. http://dx.doi.org/10.1109/access.2020.2984271.
Texto completoWai Lam. "Bayesian network refinement via machine learning approach". IEEE Transactions on Pattern Analysis and Machine Intelligence 20, n.º 3 (marzo de 1998): 240–51. http://dx.doi.org/10.1109/34.667882.
Texto completoKrems, R. V. "Bayesian machine learning for quantum molecular dynamics". Physical Chemistry Chemical Physics 21, n.º 25 (2019): 13392–410. http://dx.doi.org/10.1039/c9cp01883b.
Texto completoKarandikar, Jaydeep, Andrew Honeycutt, Scott Smith y Tony Schmitz. "Milling stability identification using Bayesian machine learning". Procedia CIRP 93 (2020): 1423–28. http://dx.doi.org/10.1016/j.procir.2020.04.022.
Texto completoBew, David, Campbell R. Harvey, Anthony Ledford, Sam Radnor y Andrew Sinclair. "Modeling Analysts’ Recommendations via Bayesian Machine Learning". Journal of Financial Data Science 1, n.º 1 (31 de enero de 2019): 75–98. http://dx.doi.org/10.3905/jfds.2019.1.1.075.
Texto completoZhu, Jun, Jianfei Chen, Wenbo Hu y Bo Zhang. "Big Learning with Bayesian methods". National Science Review 4, n.º 4 (4 de mayo de 2017): 627–51. http://dx.doi.org/10.1093/nsr/nwx044.
Texto completoBoyko, Nataliya y Oleksandra Dypko. "Analysis of Machine Learning Methods Using Spam Filtering". Modeling Control and Information Technologies, n.º 5 (21 de noviembre de 2021): 25–28. http://dx.doi.org/10.31713/mcit.2021.06.
Texto completoJ, Dr Visumathi, Tetala Durga Venkata Rama Reddy, Velagapudi Abhinandhan y Panamganti Anil Kumar. "Multi-Disease Prediction Using Machine Learning Algorithm". International Journal for Research in Applied Science and Engineering Technology 11, n.º 4 (30 de abril de 2023): 447–53. http://dx.doi.org/10.22214/ijraset.2023.50128.
Texto completoTresp, Volker. "A Bayesian Committee Machine". Neural Computation 12, n.º 11 (1 de noviembre de 2000): 2719–41. http://dx.doi.org/10.1162/089976600300014908.
Texto completoGeer, A. J. "Learning earth system models from observations: machine learning or data assimilation?" Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 379, n.º 2194 (15 de febrero de 2021): 20200089. http://dx.doi.org/10.1098/rsta.2020.0089.
Texto completoSohail, Ayesha. "INFERENCE OF BIOMEDICAL DATA SETS USING BAYESIAN MACHINE LEARNING". Biomedical Engineering: Applications, Basis and Communications 31, n.º 04 (27 de junio de 2019): 1950030. http://dx.doi.org/10.4015/s1016237219500303.
Texto completoMalviya, Ravi Prakash. "A Bayesian Machine Learning Approach for Smart City". International Journal for Research in Applied Science and Engineering Technology 9, n.º 12 (31 de diciembre de 2021): 796–816. http://dx.doi.org/10.22214/ijraset.2021.39195.
Texto completoGao, Haiping, Shifa Zhong, Wenlong Zhang, Thomas Igou, Eli Berger, Elliot Reid, Yangying Zhao et al. "Revolutionizing Membrane Design Using Machine Learning-Bayesian Optimization". Environmental Science & Technology 56, n.º 4 (30 de diciembre de 2021): 2572–81. http://dx.doi.org/10.1021/acs.est.1c04373.
Texto completoJun, Sunghae, #VALUE! #VALUE! y #VALUE! #VALUE! "Regression Machine Learning using Bayesian Inference and Regularization". Journal of Korean Institute of Intelligent Systems 29, n.º 5 (31 de octubre de 2019): 390–94. http://dx.doi.org/10.5391/jkiis.2019.29.5.390.
Texto completoWu, Wei, Srikantan Nagarajan y Zhe Chen. "Bayesian Machine Learning: EEG\/MEG signal processing measurements". IEEE Signal Processing Magazine 33, n.º 1 (enero de 2016): 14–36. http://dx.doi.org/10.1109/msp.2015.2481559.
Texto completoChakraborty, Sounak. "Bayesian semi-supervised learning with support vector machine". Statistical Methodology 8, n.º 1 (enero de 2011): 68–82. http://dx.doi.org/10.1016/j.stamet.2009.09.002.
Texto completoSarkar, Dripta, Michael A. Osborne y Thomas A. A. Adcock. "Prediction of tidal currents using Bayesian machine learning". Ocean Engineering 158 (junio de 2018): 221–31. http://dx.doi.org/10.1016/j.oceaneng.2018.03.007.
Texto completoWang, Jing, Lin Zhang, Juan-juan Cao y Di Han. "NBWELM: naive Bayesian based weighted extreme learning machine". International Journal of Machine Learning and Cybernetics 9, n.º 1 (27 de diciembre de 2014): 21–35. http://dx.doi.org/10.1007/s13042-014-0318-1.
Texto completoJiahua Luo, Chi-Man Vong y Pak-Kin Wong. "Sparse Bayesian Extreme Learning Machine for Multi-classification". IEEE Transactions on Neural Networks and Learning Systems 25, n.º 4 (abril de 2014): 836–43. http://dx.doi.org/10.1109/tnnls.2013.2281839.
Texto completoSong, Min-Jong y Yong-Sik Cho. "Probabilistic Tsunami Heights Model using Bayesian Machine Learning". Journal of Coastal Research 95, sp1 (26 de mayo de 2020): 1291. http://dx.doi.org/10.2112/si95-249.1.
Texto completoHobson, Michael, Philip Graff, Farhan Feroz y Anthony Lasenby. "Machine-learning in astronomy". Proceedings of the International Astronomical Union 10, S306 (mayo de 2014): 279–87. http://dx.doi.org/10.1017/s1743921314013672.
Texto completoWhite, Brian S., Suleiman A. Khan, Muhammad Ammad-ud-din, Swapnil Potdar, Mike J. Mason, Cristina E. Tognon, Brian J. Druker et al. "Comparative Analysis of Independent Ex Vivo functional Drug Screens Identifies Predictive Biomarkers of BCL-2 Inhibitor Response in AML". Blood 132, Supplement 1 (29 de noviembre de 2018): 2763. http://dx.doi.org/10.1182/blood-2018-99-111916.
Texto completoChavan, Mr Vikram. "Malware Classification using Machine Learning Algorithms and Tools". International Journal for Research in Applied Science and Engineering Technology 9, n.º VI (10 de junio de 2021): 69–73. http://dx.doi.org/10.22214/ijraset.2021.34353.
Texto completoNixon, Matthew C. y Nikku Madhusudhan. "Assessment of supervised machine learning for atmospheric retrieval of exoplanets". Monthly Notices of the Royal Astronomical Society 496, n.º 1 (16 de junio de 2020): 269–81. http://dx.doi.org/10.1093/mnras/staa1150.
Texto completoLehto, M. R. y G. S. Sorock. "Machine Learning of Motor Vehicle Accident Categories from Narrative Data". Methods of Information in Medicine 35, n.º 04/05 (septiembre de 1996): 309–16. http://dx.doi.org/10.1055/s-0038-1634680.
Texto completoHwang, Ha-Eun, Yoon-Sang Cho, Seok-Cheol Hwang y Seoung-Bum Kim. "Optimal Tire Design Using Machine Learning and Bayesian Optimization". Journal of the Korean Institute of Industrial Engineers 48, n.º 4 (31 de agosto de 2022): 433–40. http://dx.doi.org/10.7232/jkiie.2022.48.4.433.
Texto completoBaggio, Giacomo, Algo Carè, Anna Scampicchio y Gianluigi Pillonetto. "Bayesian frequentist bounds for machine learning and system identification". Automatica 146 (diciembre de 2022): 110599. http://dx.doi.org/10.1016/j.automatica.2022.110599.
Texto completoWilliams, Dominic P., Stanley E. Lazic, Alison J. Foster, Elizaveta Semenova y Paul Morgan. "Predicting Drug-Induced Liver Injury with Bayesian Machine Learning". Chemical Research in Toxicology 33, n.º 1 (19 de septiembre de 2019): 239–48. http://dx.doi.org/10.1021/acs.chemrestox.9b00264.
Texto completoWang, Hui. "Finding patterns in subsurface using Bayesian machine learning approach". Underground Space 5, n.º 1 (marzo de 2020): 84–92. http://dx.doi.org/10.1016/j.undsp.2018.10.006.
Texto completoWang, Jian, Ting Ran, Yadong Chen y Tao Lu. "Bayesian machine learning to discover Bruton’s tyrosine kinase inhibitors". Chemical Biology & Drug Design 96, n.º 4 (18 de agosto de 2020): 1114–22. http://dx.doi.org/10.1111/cbdd.13656.
Texto completoGarcia-Bonete, Maria-Jose y Gergely Katona. "Bayesian machine learning improves single-wavelength anomalous diffraction phasing". Acta Crystallographica Section A Foundations and Advances 75, n.º 6 (7 de octubre de 2019): 851–60. http://dx.doi.org/10.1107/s2053273319011446.
Texto completoSantucci, Raymond J., Christine E. Sanders, Hongyu Zhu, Kenneth D. Smith y Robert G. Kelly. "Bayesian Network Machine Learning Approach to Atmospheric Corrosion Modelling". ECS Meeting Abstracts MA2022-02, n.º 10 (9 de octubre de 2022): 693. http://dx.doi.org/10.1149/ma2022-0210693mtgabs.
Texto completoBessa, Miguel A., Piotr Glowacki y Michael Houlder. "Bayesian Machine Learning in Metamaterial Design: Fragile Becomes Supercompressible". Advanced Materials 31, n.º 48 (14 de octubre de 2019): 1904845. http://dx.doi.org/10.1002/adma.201904845.
Texto completoChen, Hongyu, Xinyi Li, Zongbao Feng, Lei Wang, Yawei Qin, Miroslaw J. Skibniewski, Zhen-Song Chen y Yang Liu. "Shield attitude prediction based on Bayesian-LGBM machine learning". Information Sciences 632 (junio de 2023): 105–29. http://dx.doi.org/10.1016/j.ins.2023.03.004.
Texto completoChaturvedi, Iti, Edoardo Ragusa, Paolo Gastaldo, Rodolfo Zunino y Erik Cambria. "Bayesian network based extreme learning machine for subjectivity detection". Journal of the Franklin Institute 355, n.º 4 (marzo de 2018): 1780–97. http://dx.doi.org/10.1016/j.jfranklin.2017.06.007.
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