Bibliografías temáticas / Multioutput regression

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Literatura académica sobre el tema "Multioutput regression"

Autor: Grafiati
Publicado: 30 de noviembre de 2024
Última modificación: 31 de julio de 2025

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Artículos de revistas sobre el tema "Multioutput regression"

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Tian, Qing, Meng Cao, Songcan Chen, and Hujun Yin. "Structure-Exploiting Discriminative Ordinal Multioutput Regression." IEEE Transactions on Neural Networks and Learning Systems 32, no. 1 (2021): 266–80. http://dx.doi.org/10.1109/tnnls.2020.2978508.

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Li, Shunlong, Huiming Yin, Zhonglong Li, Wencheng Xu, Yao Jin, and Shaoyang He. "Optimal sensor placement for cable force monitoring based on multioutput support vector regression model." Advances in Structural Engineering 21, no. 15 (2018): 2259–69. http://dx.doi.org/10.1177/1369433218772342.

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Cable force monitoring is an essential and critical part of structural health monitoring for cable-supported bridges. The quality of obtained information depends considerably on the number and location of limited sensors. The purpose of this article is to provide a method for optimal sensor placement for cable force monitoring in cable-supported bridges. Based on the spatial correlation between neighbouring or symmetrical cable forces, the structural information of non-monitored cables can be predicted by multioutput support vector regression models, established between monitored (input) and t
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Tuia, D., J. Verrelst, L. Alonso, F. Perez-Cruz, and G. Camps-Valls. "Multioutput Support Vector Regression for Remote Sensing Biophysical Parameter Estimation." IEEE Geoscience and Remote Sensing Letters 8, no. 4 (2011): 804–8. http://dx.doi.org/10.1109/lgrs.2011.2109934.

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KONDO, Tadashi. "Multiinput-Multioutput Type GMDH Algorithm Using Regression-Principal Component Analysis." Transactions of the Institute of Systems, Control and Information Engineers 6, no. 11 (1993): 520–29. http://dx.doi.org/10.5687/iscie.6.520.

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Yun, Seokheon. "Performance Analysis of Construction Cost Prediction Using Neural Network for Multioutput Regression." Applied Sciences 12, no. 19 (2022): 9592. http://dx.doi.org/10.3390/app12199592.

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In a construction project, construction cost estimation is very important, but construction costs are affected by various factors, so they are difficult to predict accurately. However, with the recent development of ANN technology, it has become possible to predict construction costs with consideration of various influencing factors. Unlike previous research cases, this study aimed to predict the total construction cost by predicting seven sub-construction costs using a multioutput regression model, not by predicting a single total construction cost. In addition, analysis of the change in cons
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Wang, Yu, and Guohua Liu. "MLA-TCN: Multioutput Prediction of Dam Displacement Based on Temporal Convolutional Network with Attention Mechanism." Structural Control and Health Monitoring 2023 (August 25, 2023): 1–19. http://dx.doi.org/10.1155/2023/2189912.

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The displacement of concrete dams effectively reflects their structural integrity and operational status. Therefore, establishing a model for predicting the displacement of concrete dams and studying the evolution mechanism of dam displacement is essential for monitoring the structural safety of dams. Current data-driven models utilize artificial data that cannot reflect the actual status of dams for network training. They also have difficulty extracting the temporal patterns from long-term dependencies and obtaining the interactions between the targets and variables. To address such problems,
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Avani Dedhia. "Investigation of Multi-Output Regression Modeling in Predicting Concrete Mix Design." Journal of Information Systems Engineering and Management 10, no. 43s (2025): 615–30. https://doi.org/10.52783/jisem.v10i43s.8458.

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Current research is a piece of an innovative approach to concrete mix-design prediction by implementing advanced regression techniques, that addressed the limitations of traditional methods in IS 10262 and ACI 318 standards which rely heavily on empirical relationships and require extensive trial batching. The study investigates eight important mix-design parameters namely water-cementratio, cement content, flyash content, fine aggregate content, 10 mm and 20 mm aggregate content, water content, and superplasticizer content. The methodology utilizes comprehensive Multioutput Regression with gr
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Wu, Shengbiao, Huaning Li, and Xianpeng Chen. "Parametric Model for Coaxial Cavity Filter with Combined KCCA and MLSSVR." International Journal of Antennas and Propagation 2023 (June 7, 2023): 1–10. http://dx.doi.org/10.1155/2023/2024720.

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Aiming at the problems of poor data effectiveness, low modeling accuracy, and weak generalization in the tuning process of microwave cavity filters, a parametric model for coaxial cavity filter using kernel canonical correlation analysis (KCCA) and multioutput least squares support vector regression (MLSSVR) is proposed in this study. First, the low-dimensional tuning data is mapped to the high-dimensional feature space by kernel canonical correlation analysis, and the nonlinear feature vectors are fused by the kernel function; second, the multioutput least squares support vector regression al
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Huang, Kai, Ming-Yi You, Yun-Xia Ye, Bin Jiang, and An-Nan Lu. "Direction of Arrival Based on the Multioutput Least Squares Support Vector Regression Model." Mathematical Problems in Engineering 2020 (September 30, 2020): 1–8. http://dx.doi.org/10.1155/2020/8601376.

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The interferometer is a widely used direction-finding system with high precision. When there are comprehensive disturbances in the direction-finding system, some scholars have proposed corresponding correction algorithms, but most of them require hypothesis based on the geometric position of the array. The method of using machine learning that has attracted much attention recently is data driven, which can be independent of these assumptions. We propose a direction-finding method for the interferometer by using multioutput least squares support vector regression (MLSSVR) model. The application
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Rosentreter, Johannes, Ron Hagensieker, Akpona Okujeni, Ribana Roscher, Paul D. Wagner, and Bjorn Waske. "Subpixel Mapping of Urban Areas Using EnMAP Data and Multioutput Support Vector Regression." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 10, no. 5 (2017): 1938–48. http://dx.doi.org/10.1109/jstars.2017.2652726.

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Tesis sobre el tema "Multioutput regression"

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Elimam, Rayane. "Apprentissage automatique pour la prédiction de performances : du sport à la santé." Electronic Thesis or Diss., IMT Mines Alès, 2024. https://theses.hal.science/tel-04805708.

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De nombreux indicateurs de performance existent en sport et en santé (guérison, réhabilitation, etc.) qui permettent de caractériser différents critères sportifs et thérapeutiques.Ces différents types de performance dépendent généralement de la charge de travail (ou de rééducation) subie par les sportifs ou patients.Ces dernières années, beaucoup d'applications de l'apprentissage automatique au sport et à la santé ont été proposées.La prédiction, voir l'explication de performances à partir de données de charges pourrait permettre d'optimiser les entraînements ou les thérapies.Dans ce contexte
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Capítulos de libros sobre el tema "Multioutput regression"

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Silalahi, Margaretha Gracia Hotmatua, Muhammad Ahsan, and Muhammad Hisyam Lee. "Statistical Quality Control of NPK Fertilizer Production Process using Mixed Dual Multivariate Cumulative Sum (MDMCUSUM) Chart based on Multioutput Least Square Support Vector Regression (MLS-SVR)." In Advances in Computer Science Research. Atlantis Press International BV, 2023. http://dx.doi.org/10.2991/978-94-6463-332-0_2.

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Actas de conferencias sobre el tema "Multioutput regression"

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emami, seyedsaman, and Gonzalo Martínez-Muñoz. "Multioutput Regression Neural Network Training via Gradient Boosting." In ESANN 2022 - European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Ciaco - i6doc.com, 2022. http://dx.doi.org/10.14428/esann/2022.es2022-95.

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Shao, Yiping, Shichang Du, and Lifeng Xi. "3D Machined Surface Topography Forecasting With Space-Time Multioutput Support Vector Regression Using High Definition Metrology." In ASME 2017 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/detc2017-67155.

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Satisfied surface topography is important to achieve the function of a part, thereby machined surface prediction is essential. A surface forecasting model called space-time multioutput support vector regression (STMSVR) is developed in this paper. With machined surfaces pervading in manufacturing, high definition metrology (HDM) is adopted to measure the three dimensional machined surface. Millions of data points are generated to represent the entire surface. The STMSVR model captures the spatial-temporal characteristics of the successively machined surface and predicts the future surface. To
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Gainitdinov, Batyrkhan, Yury Meshalkina, Denis Orlova, et al. "Predicting Mineralogical Composition in Unconventional Formations Using Machine Learning and Well Logging Data." In International Petroleum Technology Conference. IPTC, 2024. http://dx.doi.org/10.2523/iptc-23487-ea.

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Abstract Quantitative determination of mineralogy can be done using high-definition spectroscopic logging methods, however these methods are rarely used due to complexity and cost. Also, it is difficult to obtain mineralogical composition in unconventional formations due to presence of kerogen and high heterogeneity and anisotropy of such formations. This problem can be resolved by utilizing Machine Learning algorithms based on well logging and thermal profiling data which can improve and speed up reservoir characterisation. Special wrappers such as Multioutput Regressor and Regressor Chain we
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