Literatura científica selecionada sobre o tema "Machine Learning, Graphical Models, Kernel Methods, Optimization"
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Artigos de revistas sobre o assunto "Machine Learning, Graphical Models, Kernel Methods, Optimization":
Deist, Timo M., Andrew Patti, Zhaoqi Wang, David Krane, Taylor Sorenson e David Craft. "Simulation-assisted machine learning". Bioinformatics 35, n.º 20 (23 de março de 2019): 4072–80. http://dx.doi.org/10.1093/bioinformatics/btz199.
Özöğür Akyüz, Süreyya, Gürkan Üstünkar e Gerhard Wilhelm Weber. "Adapted Infinite Kernel Learning by Multi-Local Algorithm". International Journal of Pattern Recognition and Artificial Intelligence 30, n.º 04 (12 de abril de 2016): 1651004. http://dx.doi.org/10.1142/s0218001416510046.
Lu, Shengfu, Sa Liu, Mi Li, Xin Shi e Richeng Li. "Depression Classification Model Based on Emotionally Related Eye-Movement Data and Kernel Extreme Learning Machine". Journal of Medical Imaging and Health Informatics 10, n.º 11 (1 de novembro de 2020): 2668–74. http://dx.doi.org/10.1166/jmihi.2020.3198.
SEEGER, MATTHIAS. "GAUSSIAN PROCESSES FOR MACHINE LEARNING". International Journal of Neural Systems 14, n.º 02 (abril de 2004): 69–106. http://dx.doi.org/10.1142/s0129065704001899.
Abdelhamid, Abdelaziz A., El-Sayed M. El El-Kenawy, Abdelhameed Ibrahim e Marwa M. Eid. "Intelligent Wheat Types Classification Model Using New Voting Classifier". Journal of Intelligent Systems and Internet of Things 7, n.º 1 (2022): 30–39. http://dx.doi.org/10.54216/jisiot.070103.
Ramasamy, Lakshmana Kumar, Seifedine Kadry e Sangsoon Lim. "Selection of optimal hyper-parameter values of support vector machine for sentiment analysis tasks using nature-inspired optimization methods". Bulletin of Electrical Engineering and Informatics 10, n.º 1 (1 de fevereiro de 2021): 290–98. http://dx.doi.org/10.11591/eei.v10i1.2098.
Zhao, Xutao, Desheng Zhang, Renhui Zhang e Bin Xu. "A comparative study of Gaussian process regression with other three machine learning approaches in the performance prediction of centrifugal pump". Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 236, n.º 8 (30 de dezembro de 2021): 3938–49. http://dx.doi.org/10.1177/09544062211050542.
Alarfaj, Fawaz Khaled, Naveed Ahmad Khan, Muhammad Sulaiman e Abdullah M. Alomair. "Application of a Machine Learning Algorithm for Evaluation of Stiff Fractional Modeling of Polytropic Gas Spheres and Electric Circuits". Symmetry 14, n.º 12 (23 de novembro de 2022): 2482. http://dx.doi.org/10.3390/sym14122482.
Mei, Wenjuan, Zhen Liu, Yuanzhang Su, Li Du e Jianguo Huang. "Evolved-Cooperative Correntropy-Based Extreme Learning Machine for Robust Prediction". Entropy 21, n.º 9 (19 de setembro de 2019): 912. http://dx.doi.org/10.3390/e21090912.
Correa-Jullian, Camila, Sergio Cofre-Martel, Gabriel San Martin, Enrique Lopez Droguett, Gustavo de Novaes Pires Leite e Alexandre Costa. "Exploring Quantum Machine Learning and Feature Reduction Techniques for Wind Turbine Pitch Fault Detection". Energies 15, n.º 8 (11 de abril de 2022): 2792. http://dx.doi.org/10.3390/en15082792.
Teses / dissertações sobre o assunto "Machine Learning, Graphical Models, Kernel Methods, Optimization":
Zhang, Xinhua, e xinhua zhang cs@gmail com. "Graphical Models: Modeling, Optimization, and Hilbert Space Embedding". The Australian National University. ANU College of Engineering and Computer Sciences, 2010. http://thesis.anu.edu.au./public/adt-ANU20100729.072500.
Rowland, Mark. "Structure in machine learning : graphical models and Monte Carlo methods". Thesis, University of Cambridge, 2018. https://www.repository.cam.ac.uk/handle/1810/287479.
Zhang, Xinhua. "Graphical Models: Modeling, Optimization, and Hilbert Space Embedding". Phd thesis, 2010. http://hdl.handle.net/1885/49340.
Capítulos de livros sobre o assunto "Machine Learning, Graphical Models, Kernel Methods, Optimization":
Dral, Pavlo O., Fuchun Ge, Bao Xin Xue, Yi-Fan Hou, Max Pinheiro, Jianxing Huang e Mario Barbatti. "MLatom 2: An Integrative Platform for Atomistic Machine Learning". In Topics in Current Chemistry Collections, 13–53. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-07658-9_2.
Trabalhos de conferências sobre o assunto "Machine Learning, Graphical Models, Kernel Methods, Optimization":
Adeeyo, Yisa Ademola, Anuola Ayodeji Osinaike e Gamaliel Olawale Adun. "Estimation of Fluid Saturation Using Machine Learning Algorithms: A Case Study of Niger Delta Sandstone Reservoirs". In SPE Reservoir Characterisation and Simulation Conference and Exhibition. SPE, 2023. http://dx.doi.org/10.2118/212696-ms.
Wang, Liwei, Suraj Yerramilli, Akshay Iyer, Daniel Apley, Ping Zhu e Wei Chen. "Data-Driven Design via Scalable Gaussian Processes for Multi-Response Big Data With Qualitative Factors". In ASME 2021 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2021. http://dx.doi.org/10.1115/detc2021-71570.