Literatura académica sobre el tema "Kernel-based model"
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Artículos de revistas sobre el tema "Kernel-based model"
Nishiyama, Yu, Motonobu Kanagawa, Arthur Gretton y Kenji Fukumizu. "Model-based kernel sum rule: kernel Bayesian inference with probabilistic models". Machine Learning 109, n.º 5 (2 de enero de 2020): 939–72. http://dx.doi.org/10.1007/s10994-019-05852-9.
Texto completoZong, Xinlu, Chunzhi Wang y Hui Xu. "Density-based Adaptive Wavelet Kernel SVM Model for P2P Traffic Classification". International Journal of Future Generation Communication and Networking 6, n.º 6 (31 de diciembre de 2013): 25–36. http://dx.doi.org/10.14257/ijfgcn.2013.6.6.04.
Texto completoShim, Jooyong y Changha Hwang. "Kernel-based orthogonal quantile regression model". Model Assisted Statistics and Applications 12, n.º 3 (30 de agosto de 2017): 217–26. http://dx.doi.org/10.3233/mas-170396.
Texto completoSu, Zhi-gang, Pei-hong Wang y Zhao-long Song. "Kernel based nonlinear fuzzy regression model". Engineering Applications of Artificial Intelligence 26, n.º 2 (febrero de 2013): 724–38. http://dx.doi.org/10.1016/j.engappai.2012.05.009.
Texto completoWang, Zhijie, Mohamed Ben Salah, Hong Zhang y Nilanjan Ray. "Shape based appearance model for kernel tracking". Image and Vision Computing 30, n.º 4-5 (mayo de 2012): 332–44. http://dx.doi.org/10.1016/j.imavis.2012.03.003.
Texto completoMa, Xin y Zhi-bin Liu. "The kernel-based nonlinear multivariate grey model". Applied Mathematical Modelling 56 (abril de 2018): 217–38. http://dx.doi.org/10.1016/j.apm.2017.12.010.
Texto completoLingyu, Liang, Wenqi Huang, Zhaojie Dong, Jiguang Zhao, Peng Li, Bingfang Lu y Xinde Zhu. "Short-term power load forecasting based on combined kernel Gaussian process hybrid model". E3S Web of Conferences 256 (2021): 01009. http://dx.doi.org/10.1051/e3sconf/202125601009.
Texto completoFan, Yanqin y Qi Li. "CONSISTENT MODEL SPECIFICATION TESTS". Econometric Theory 16, n.º 6 (diciembre de 2000): 1016–41. http://dx.doi.org/10.1017/s0266466600166083.
Texto completoZhai, Yuejing, Zhouzheng Li y Haizhong Liu. "Multi-Angle Fast Neural Tangent Kernel Classifier". Applied Sciences 12, n.º 21 (26 de octubre de 2022): 10876. http://dx.doi.org/10.3390/app122110876.
Texto completoSegera, Davies, Mwangi Mbuthia y Abraham Nyete. "Particle Swarm Optimized Hybrid Kernel-Based Multiclass Support Vector Machine for Microarray Cancer Data Analysis". BioMed Research International 2019 (16 de diciembre de 2019): 1–11. http://dx.doi.org/10.1155/2019/4085725.
Texto completoTesis sobre el tema "Kernel-based model"
Bose, Aishwarya. "Effective web service discovery using a combination of a semantic model and a data mining technique". Thesis, Queensland University of Technology, 2008. https://eprints.qut.edu.au/26425/1/Aishwarya_Bose_Thesis.pdf.
Texto completoBose, Aishwarya. "Effective web service discovery using a combination of a semantic model and a data mining technique". Queensland University of Technology, 2008. http://eprints.qut.edu.au/26425/.
Texto completoZhang, Lin. "Semiparametric Bayesian Kernel Survival Model for Highly Correlated High-Dimensional Data". Diss., Virginia Tech, 2018. http://hdl.handle.net/10919/95040.
Texto completoPHD
Garg, Aditie. "Designing Reactive Power Control Rules for Smart Inverters using Machine Learning". Thesis, Virginia Tech, 2018. http://hdl.handle.net/10919/83558.
Texto completoMaster of Science
Kim, Byung-Jun. "Semiparametric and Nonparametric Methods for Complex Data". Diss., Virginia Tech, 2020. http://hdl.handle.net/10919/99155.
Texto completoDoctor of Philosophy
A variety of complex data has broadened in many research fields such as epidemiology, genomics, and analytical chemistry with the development of science, technologies, and design scheme over the past few decades. For example, in epidemiology, the matched case-crossover study design is used to investigate the association between the clustered binary outcomes of disease and a measurement error in covariate within a certain period by stratifying subjects' conditions. In genomics, high-correlated and high-dimensional(HCHD) data are required to identify important genes and their interaction effect over diseases. In analytical chemistry, multiple time series data are generated to recognize the complex patterns among multiple classes. Due to the great diversity, we encounter three problems in analyzing the following three types of data: (1) matched case-crossover data, (2) HCHD data, and (3) Time-series data. We contribute to the development of statistical methods to deal with such complex data. First, under the matched study, we discuss an idea about hypothesis testing to effectively determine the association between observed factors and risk of interested disease. Because, in practice, we do not know the specific form of the association, it might be challenging to set a specific alternative hypothesis. By reflecting the reality, we consider the possibility that some observations are measured with errors. By considering these measurement errors, we develop a testing procedure under the matched case-crossover framework. This testing procedure has the flexibility to make inferences on various hypothesis settings. Second, we consider the data where the number of variables is very large compared to the sample size, and the variables are correlated to each other. In this case, our goal is to identify important variables for outcome among a large amount of the variables and build their network. For example, identifying few genes among whole genomics associated with diabetes can be used to develop biomarkers. By our proposed approach in the second project, we can identify differentially expressed and important genes and their network structure with consideration for the outcome. Lastly, we consider the scenario of changing patterns of interest over time with application to gas chromatography. We propose an efficient detection method to effectively distinguish the patterns of multi-level subjects in time-trend analysis. We suggest that our proposed method can give precious information on efficient search for the distinguishable patterns so as to reduce the burden of examining all observations in the data.
Polajnar, Tamara. "Semantic models as metrics for kernel-based interaction identification". Thesis, University of Glasgow, 2010. http://theses.gla.ac.uk/2260/.
Texto completoLyubchyk, Leonid, Oleksy Galuza y Galina Grinberg. "Ranking Model Real-Time Adaptation via Preference Learning Based on Dynamic Clustering". Thesis, ННК "IПСА" НТУУ "КПI iм. Iгоря Сiкорського", 2017. http://repository.kpi.kharkov.ua/handle/KhPI-Press/36819.
Texto completoVlachos, Dimitrios. "Novel algorithms in wireless CDMA systems for estimation and kernel based equalization". Thesis, Brunel University, 2012. http://bura.brunel.ac.uk/handle/2438/7658.
Texto completoBuch, Armin [Verfasser] y Gerhard [Akademischer Betreuer] Jäger. "Linguistic Spaces : Kernel-based models of natural language / Armin Buch ; Betreuer: Gerhard Jäger". Tübingen : Universitätsbibliothek Tübingen, 2011. http://d-nb.info/1161803572/34.
Texto completoMahfouz, Sandy. "Kernel-based machine learning for tracking and environmental monitoring in wireless sensor networkds". Thesis, Troyes, 2015. http://www.theses.fr/2015TROY0025/document.
Texto completoThis thesis focuses on the problems of localization and gas field monitoring using wireless sensor networks. First, we focus on the geolocalization of sensors and target tracking. Using the powers of the signals exchanged between sensors, we propose a localization method combining radio-location fingerprinting and kernel methods from statistical machine learning. Based on this localization method, we develop a target tracking method that enhances the estimated position of the target by combining it to acceleration information using the Kalman filter. We also provide a semi-parametric model that estimates the distances separating sensors based on the powers of the signals exchanged between them. This semi-parametric model is a combination of the well-known log-distance propagation model with a non-linear fluctuation term estimated within the framework of kernel methods. The target's position is estimated by incorporating acceleration information to the distances separating the target from the sensors, using either the Kalman filter or the particle filter. In another context, we study gas diffusions in wireless sensor networks, using also machine learning. We propose a method that allows the detection of multiple gas diffusions based on concentration measures regularly collected from the studied region. The method estimates then the parameters of the multiple gas sources, including the sources' locations and their release rates
Capítulos de libros sobre el tema "Kernel-based model"
Chen, Bo, Hongwei Liu y Zheng Bao. "General Kernel Optimization Model Based on Kernel Fisher Criterion". En Lecture Notes in Computer Science, 143–46. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11881070_24.
Texto completoZhang, Yuehua, Peng Zhang y Yong Shi. "Kernel Based Regularized Multiple Criteria Linear Programming Model". En Lecture Notes in Computer Science, 625–32. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01973-9_70.
Texto completoTravieso, Carlos M., Jesús B. Alonso, Jaime R. Ticay-Rivas y Marcos del Pozo-Baños. "Apnea Detection Based on Hidden Markov Model Kernel". En Advances in Nonlinear Speech Processing, 71–79. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-25020-0_10.
Texto completoFleischanderl, Gerhard, Thomas Havelka, Herwig Schreiner, Markus Stumptner y Franz Wotawa. "DiKe - A Model-Based Diagnosis Kernel and Its Application". En KI 2001: Advances in Artificial Intelligence, 440–54. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-45422-5_31.
Texto completoTaylan, Pakize. "Kernel Based C-Bridge Estimator for Partially Nonlinear Model". En Operations Research, 2–20. Boca Raton: CRC Press, 2022. http://dx.doi.org/10.1201/9781003324508-2.
Texto completoHernández-Torruco, José, Juana Canul-Reich, Juan Frausto-Solis y Juan José Méndez-Castillo. "A Kernel-Based Predictive Model for Guillain-Barré Syndrome". En Advances in Artificial Intelligence and Its Applications, 270–81. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-27101-9_20.
Texto completoKokologiannakis, Michalis y Viktor Vafeiadis. "GenMC: A Model Checker for Weak Memory Models". En Computer Aided Verification, 427–40. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-81685-8_20.
Texto completoZhou, Yifei y Conor Hayes. "Graph-Based Diffusion Method for Top-N Recommendation". En Communications in Computer and Information Science, 292–304. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-26438-2_23.
Texto completoDudek, Grzegorz. "Variable Selection in the Kernel Regression Based Short-Term Load Forecasting Model". En Artificial Intelligence and Soft Computing, 557–63. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-29350-4_66.
Texto completoJing, Huiyun, Xin He, Qi Han y Xiamu Niu. "A Saliency Detection Model Based on Local and Global Kernel Density Estimation". En Neural Information Processing, 164–71. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24955-6_20.
Texto completoActas de conferencias sobre el tema "Kernel-based model"
Yu, Leiming, Xun Gong, Yifan Sun, Qianqian Fang, Norm Rubin y David Kaeli. "Moka: Model-based concurrent kernel analysis". En 2017 IEEE International Symposium on Workload Characterization (IISWC). IEEE, 2017. http://dx.doi.org/10.1109/iiswc.2017.8167777.
Texto completoZhu, Qi, Yong Xu, JinRong Cui, ChangFeng Chen, JingHua Wang, XiangQian Wu y YingNan Zhao. "A method for constructing simplified kernel model based on kernel-MSE". En 2009 Asia-Pacific Conference on Computational Intelligence and Industrial Applications (PACIIA 2009). IEEE, 2009. http://dx.doi.org/10.1109/paciia.2009.5406447.
Texto completoChen, Shan, Lingling Zhou, Rendong Ying y Yi Ge. "Safe device driver model based on kernel-mode JVM". En the 3rd international workshop. New York, New York, USA: ACM Press, 2007. http://dx.doi.org/10.1145/1408654.1408657.
Texto completoGhoshal, Debarshi Patanjali, Kumar Gopalakrishnan y Hannah Michalska. "Kernel-based adaptive multiple model target tracking". En 2017 IEEE Conference on Control Technology and Applications (CCTA). IEEE, 2017. http://dx.doi.org/10.1109/ccta.2017.8062644.
Texto completoFang, Yudong, Zhenfei Zhan, Junqi Yang, Jun Lu y Chong Chen. "A Mixed-Kernel-Based Support Vector Regression Model for Automotive Body Design Optimization". En ASME 2016 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/imece2016-67669.
Texto completoLangone, Rocco, Carlos Alzate y Johan A. K. Suykens. "Modularity-based model selection for kernel spectral clustering". En 2011 International Joint Conference on Neural Networks (IJCNN 2011 - San Jose). IEEE, 2011. http://dx.doi.org/10.1109/ijcnn.2011.6033449.
Texto completoChen, Huanhuan, Fengzhen Tang, Peter Tino y Xin Yao. "Model-based kernel for efficient time series analysis". En KDD' 13: The 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, NY, USA: ACM, 2013. http://dx.doi.org/10.1145/2487575.2487700.
Texto completoBeckers, Thomas, Somil Bansal, Claire J. Tomlin y Sandra Hirche. "Closed-loop Model Selection for Kernel-based Models using Bayesian Optimization". En 2019 IEEE 58th Conference on Decision and Control (CDC). IEEE, 2019. http://dx.doi.org/10.1109/cdc40024.2019.9029690.
Texto completoJadhav, Dattatray V. y Raghunath S. Holambe. "Multiresolution based Kernel Fisher Discriminant Model for Face Recognition". En Fourth International Conference on Information Technology (ITNG'07). IEEE, 2007. http://dx.doi.org/10.1109/itng.2007.131.
Texto completoJanakiram, Dharanipragada, Hemang Mehta y S. J. Balaji. "Dhara: A Service Abstraction-Based OS Kernel Design Model". En 2012 17th International Conference on Engineering of Complex Computer Systems (ICECCS). IEEE, 2012. http://dx.doi.org/10.1109/iceccs20050.2012.6299208.
Texto completoInformes sobre el tema "Kernel-based model"
Helmut, Harbrecht, John Davis Jakeman y Peter Zaspel. Weighted greedy-optimal design of computer experiments for kernel-based and Gaussian process model emulation and calibration. Office of Scientific and Technical Information (OSTI), marzo de 2020. http://dx.doi.org/10.2172/1608084.
Texto completoSparks, Paul, Jesse Sherburn, William Heard y Brett Williams. Penetration modeling of ultra‐high performance concrete using multiscale meshfree methods. Engineer Research and Development Center (U.S.), septiembre de 2021. http://dx.doi.org/10.21079/11681/41963.
Texto completoManninen, Terhikki y Pauline Stenberg. Influence of forest floor vegetation on the total forest reflectance and its implications for LAI estimation using vegetation indices. Finnish Meteorological Institute, 2021. http://dx.doi.org/10.35614/isbn.9789523361379.
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