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Статті в журналах з теми "Gaussian Regression Processes"
Boloix-Tortosa, Rafael, Juan Jose Murillo-Fuentes, Francisco Javier Payan-Somet, and Fernando Perez-Cruz. "Complex Gaussian Processes for Regression." IEEE Transactions on Neural Networks and Learning Systems 29, no. 11 (November 2018): 5499–511. http://dx.doi.org/10.1109/tnnls.2018.2805019.
Повний текст джерелаMunoz-Gonzalez, Luis, Miguel Lazaro-Gredilla, and Anibal R. Figueiras-Vidal. "Divisive Gaussian Processes for Nonstationary Regression." IEEE Transactions on Neural Networks and Learning Systems 25, no. 11 (November 2014): 1991–2003. http://dx.doi.org/10.1109/tnnls.2014.2301951.
Повний текст джерелаTerry, Nick, and Youngjun Choe. "Splitting Gaussian processes for computationally-efficient regression." PLOS ONE 16, no. 8 (August 24, 2021): e0256470. http://dx.doi.org/10.1371/journal.pone.0256470.
Повний текст джерелаWu, Xing Hui, and Yu Ping Zhou. "Regression and Classification Method Based on Gaussian Processes." Advanced Materials Research 971-973 (June 2014): 1949–52. http://dx.doi.org/10.4028/www.scientific.net/amr.971-973.1949.
Повний текст джерелаGonçalves, Ítalo Gomes, Felipe Guadagnin, and Diogo Peixoto Cordova. "Learning spatial patterns with variational Gaussian processes: Regression." Computers & Geosciences 161 (April 2022): 105056. http://dx.doi.org/10.1016/j.cageo.2022.105056.
Повний текст джерелаPerez-Cruz, F., J. J. Murillo-Fuentes, and S. Caro. "Nonlinear Channel Equalization With Gaussian Processes for Regression." IEEE Transactions on Signal Processing 56, no. 10 (October 2008): 5283–86. http://dx.doi.org/10.1109/tsp.2008.928512.
Повний текст джерелаZhang, Tong. "Approximation Bounds for Some Sparse Kernel Regression Algorithms." Neural Computation 14, no. 12 (December 1, 2002): 3013–42. http://dx.doi.org/10.1162/089976602760805395.
Повний текст джерелаCarvalho, Ruan M., Iago G. L. Rosa, Diego E. B. Gomes, Priscila V. Z. C. Goliatt, and Leonardo Goliatt. "Gaussian processes regression for cyclodextrin host-guest binding prediction." Journal of Inclusion Phenomena and Macrocyclic Chemistry 101, no. 1-2 (July 12, 2021): 149–59. http://dx.doi.org/10.1007/s10847-021-01092-4.
Повний текст джерелаMunoz-Gonzalez, Luis, Miguel Lazaro-Gredilla, and Anibal R. Figueiras-Vidal. "Laplace Approximation for Divisive Gaussian Processes for Nonstationary Regression." IEEE Transactions on Pattern Analysis and Machine Intelligence 38, no. 3 (March 1, 2016): 618–24. http://dx.doi.org/10.1109/tpami.2015.2452914.
Повний текст джерелаLeithead, W. E., Kian Seng Neo, and D. J. Leith. "GAUSSIAN REGRESSION BASED ON MODELS WITH TWO STOCHASTIC PROCESSES." IFAC Proceedings Volumes 38, no. 1 (2005): 142–47. http://dx.doi.org/10.3182/20050703-6-cz-1902.00024.
Повний текст джерелаДисертації з теми "Gaussian Regression Processes"
Beck, Daniel Emilio. "Gaussian processes for text regression." Thesis, University of Sheffield, 2017. http://etheses.whiterose.ac.uk/17619/.
Повний текст джерелаGibbs, M. N. "Bayesian Gaussian processes for regression and classification." Thesis, University of Cambridge, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.599379.
Повний текст джерелаWågberg, Johan, and Viklund Emanuel Walldén. "Continuous Occupancy Mapping Using Gaussian Processes." Thesis, Linköpings universitet, Reglerteknik, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-81464.
Повний текст джерелаDavies, Alexander James. "Effective implementation of Gaussian process regression for machine learning." Thesis, University of Cambridge, 2015. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.708909.
Повний текст джерела余瑞心 and Sui-sum Amy Yu. "Application of Markov regression models in non-Gaussian time series analysis." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1991. http://hub.hku.hk/bib/B31976840.
Повний текст джерелаRasmussen, Carl Edward. "Evaluation of Gaussian processes and other methods for non-linear regression." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/nq28300.pdf.
Повний текст джерелаSun, Furong. "Some Advances in Local Approximate Gaussian Processes." Diss., Virginia Tech, 2019. http://hdl.handle.net/10919/97245.
Повний текст джерелаDoctor of Philosophy
In many real-life settings, we want to understand a physical relationship/phenomenon. Due to limited resources and/or ethical reasons, it is impossible to perform physical experiments to collect data, and therefore, we have to rely upon computer experiments, whose evaluation usually requires expensive simulation, involving complex mathematical equations. To reduce computational efforts, we are looking for a relatively cheap alternative, which is called an emulator, to serve as a surrogate model. Gaussian process (GP) is such an emulator, and has been very popular due to fabulous out-of-sample predictive performance and appropriate uncertainty quantification. However, due to computational complexity, full GP modeling is not suitable for “big data” settings. Gramacy and Apley (2015) proposed local approximate GP (laGP), the core idea of which is to use a subset of the data for inference and further prediction at unobserved inputs. This dissertation provides several extensions of laGP, which are applied to several real-life “big data” settings. The first application, detailed in Chapter 3, is to emulate satellite drag from large simulation experiments. A smart way is figured out to capture global input information in a comprehensive way by using a small subset of the data, and local prediction is performed subsequently. This method is called “multilevel GP modeling”, which is also deployed to synthesize field measurements and computational outputs of solar irradiance across the continental United States, illustrated in Chapter 4, and to emulate daytime land surface temperatures estimated by satellites, discussed in Chapter 5.
Zertuche, Federico. "Utilisation de simulateurs multi-fidélité pour les études d'incertitudes dans les codes de caclul." Thesis, Université Grenoble Alpes (ComUE), 2015. http://www.theses.fr/2015GREAM069/document.
Повний текст джерелаA very important tool used by applied mathematicians and engineers to model the behavior of a system are computer simulations. They have become increasingly more precise but also more complicated. So much, that they are very slow to produce an output and thus difficult to sample so that many aspects of these simulations are not very well understood. For example, in many cases they depend on parameters whose value isA metamodel is a reconstruction of the simulation. It requires much less time to produce an output that is close to what the simulation would. By using it, some aspects of the original simulation can be studied. It is built with very few samples and its purpose is to replace the simulation.This thesis is concerned with the construction of a metamodel in a particular context called multi-fidelity. In multi-fidelity the metamodel is constructed using the data from the target simulation along other samples that are related. These approximate samples can come from a degraded version of the simulation; an old version that has been studied extensively or a another simulation in which a part of the description is simplified.By learning the difference between the samples it is possible to incorporate the information of the approximate data and this may lead to an enhanced metamodel. In this manuscript two approaches that do this are studied: one based on Gaussian process modeling and another based on a coarse to fine Wavelet decomposition. The fist method shows how by estimating the relationship between two data sets it is possible to incorporate data that would be useless otherwise. In the second method an adaptive procedure to add data systematically to enhance the metamodel is proposed.The object of this work is to better our comprehension of how to incorporate approximate data to enhance a metamodel. Working with a multi-fidelity metamodel helps us to understand in detail the data that nourish it. At the end a global picture of the elements that compose it is formed: the relationship and the differences between all the data sets become clearer
Wikland, Love. "Early-Stage Prediction of Lithium-Ion Battery Cycle Life Using Gaussian Process Regression." Thesis, KTH, Matematisk statistik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-273619.
Повний текст джерелаDatadriven prediktion av batterihälsa har fått ökad uppmärksamhet under de senaste åren, både inom akademin och industrin. Precisa prediktioner i tidigt stadium av batteriprestanda skulle kunna skapa nya möjligheter för produktion och användning. Genom att använda data från endast de första 100 cyklerna, i en datamängd med 124 celler där livslängden sträcker sig mellan 150 och 2300 cykler, kombinerar denna uppsats parametriska linjära modeller med ickeparametrisk Gaussisk processregression för att uppnå livstidsprediktioner med en genomsnittlig noggrannhet om 8.8% fel. Studien utgör ett relevant bidrag till den aktuella forskningen eftersom den använda kombinationen av metoder inte tidigare utnyttjats för regression av batterilivslängd med ett högdimensionellt variabelrum. Studien och de erhållna resultaten visar att regression med hjälp av Gaussiska processer kan bidra i framtida datadrivna implementeringar av prediktion för batterihälsa.
Persson, Lejon Ludvig, and Fredrik Berntsson. "Regression Analysis on NBA Players Background and Performance using Gaussian Processes : Can NBA-drafts be improved by taking socioeconomic background into consideration?" Thesis, KTH, Skolan för teknikvetenskap (SCI), 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-153767.
Повний текст джерелаКниги з теми "Gaussian Regression Processes"
Taeryon, Choi, ed. Gaussian process regression analysis for functional data. Boca Raton, FL: CRC Press, 2011.
Знайти повний текст джерелаRasmussen, Carl Edward. Evaluation of Gaussian processes and other methods for non-linear regression. Toronto: University of Toronto, Dept. of Computer Science, 1997.
Знайти повний текст джерелаNeal, Radford M. Monte Carlo implementation of Gaussian process models for Bayesian regression and classification. Toronto: University of Toronto, 1997.
Знайти повний текст джерелаApplied parameter estimation for chemical engineers. New York: Marcel Dekker, 2001.
Знайти повний текст джерелаShi, Jian Qing, and Taeryon Choi. Gaussian Process Regression Analysis for Functional Data. Taylor & Francis Group, 2011.
Знайти повний текст джерелаShi, Jian Qing, and Taeryon Choi. Gaussian Process Regression Analysis for Functional Data. Taylor & Francis Group, 2011.
Знайти повний текст джерелаShi, Jian Qing, and Taeryon Choi. Gaussian Process Regression Analysis for Functional Data. Taylor & Francis Group, 2011.
Знайти повний текст джерелаFaraway, Julian J., Xiaofeng Wang, and Yu Ryan Yue. Bayesian Regression Modeling with INLA. Taylor & Francis Group, 2018.
Знайти повний текст джерелаFaraway, Julian J., Xiaofeng Wang, and Yu Ryan Yue. Bayesian Regression Modeling with INLA. Taylor & Francis Group, 2018.
Знайти повний текст джерелаBayesian Regression Modeling with INLA. Taylor & Francis Group, 2018.
Знайти повний текст джерелаЧастини книг з теми "Gaussian Regression Processes"
Williams, Christopher K. I. "Regression with Gaussian Processes." In Mathematics of Neural Networks, 378–82. Boston, MA: Springer US, 1997. http://dx.doi.org/10.1007/978-1-4615-6099-9_66.
Повний текст джерелаDixon, Matthew F., Igor Halperin, and Paul Bilokon. "Bayesian Regression and Gaussian Processes." In Machine Learning in Finance, 81–109. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-41068-1_3.
Повний текст джерелаLuschgy, Harald. "Ordering regression models of Gaussian processes." In Stochastic orders and decision under risk, 207–30. Hayward, CA: Institute of Mathematical Statistics, 1991. http://dx.doi.org/10.1214/lnms/1215459858.
Повний текст джерелаSrijith, P. K., Shirish Shevade, and S. Sundararajan. "Validation Based Sparse Gaussian Processes for Ordinal Regression." In Neural Information Processing, 409–16. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-34481-7_50.
Повний текст джерелаGao, Jin, Haibin Ling, Weiming Hu, and Junliang Xing. "Transfer Learning Based Visual Tracking with Gaussian Processes Regression." In Computer Vision – ECCV 2014, 188–203. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-10578-9_13.
Повний текст джерелаSudhaman, K., Mahesh Akuthota, and Sandip Kumar Chaurasiya. "A Review on the Different Regression Analysis in Supervised Learning." In Bayesian Reasoning and Gaussian Processes for Machine Learning Applications, 15–32. Boca Raton: Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003164265-2.
Повний текст джерелаPilon, Bruno H. A., Juan J. Murillo-Fuentes, João Paulo C. L. da Costa, Rafael T. de Sousa Júnior, and Antonio M. R. Serrano. "Predictive Analytics in Business Intelligence Systems via Gaussian Processes for Regression." In Communications in Computer and Information Science, 421–42. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-52758-1_23.
Повний текст джерелаQiang, Zhe, and Jinwen Ma. "Automatic Model Selection of the Mixtures of Gaussian Processes for Regression." In Advances in Neural Networks – ISNN 2015, 335–44. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-25393-0_37.
Повний текст джерелаWilliams, C. K. I. "Prediction with Gaussian Processes: From Linear Regression to Linear Prediction and Beyond." In Learning in Graphical Models, 599–621. Dordrecht: Springer Netherlands, 1998. http://dx.doi.org/10.1007/978-94-011-5014-9_23.
Повний текст джерелаAntunes, Francisco, Aidan O’Sullivan, Filipe Rodrigues, and Francisco Pereira. "A Review of Heteroscedasticity Treatment with Gaussian Processes and Quantile Regression Meta-models." In Springer Geography, 141–60. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-40902-3_9.
Повний текст джерелаТези доповідей конференцій з теми "Gaussian Regression Processes"
Calandra, Roberto, Jan Peters, Carl Edward Rasmussen, and Marc Peter Deisenroth. "Manifold Gaussian Processes for regression." In 2016 International Joint Conference on Neural Networks (IJCNN). IEEE, 2016. http://dx.doi.org/10.1109/ijcnn.2016.7727626.
Повний текст джерелаGuerrero, Pablo, and Javier Ruiz del Solar. "Circular Regression Based on Gaussian Processes." In 2014 22nd International Conference on Pattern Recognition (ICPR). IEEE, 2014. http://dx.doi.org/10.1109/icpr.2014.631.
Повний текст джерелаLe, Trung, Khanh Nguyen, Vu Nguyen, Tu Dinh Nguyen, and Dinh Phung. "GoGP: Fast Online Regression with Gaussian Processes." In 2017 IEEE International Conference on Data Mining (ICDM). IEEE, 2017. http://dx.doi.org/10.1109/icdm.2017.35.
Повний текст джерелаEcheverria Rios, Diego, and Peter Green. "GAUSSIAN PROCESSES FOR REGRESSION AND CLASSIFICATION TASKS USING NON-GAUSSIAN LIKELIHOODS." In 2nd International Conference on Uncertainty Quantification in Computational Sciences and Engineering. Athens: Institute of Structural Analysis and Antiseismic Research School of Civil Engineering National Technical University of Athens (NTUA) Greece, 2019. http://dx.doi.org/10.7712/120219.6359.18465.
Повний текст джерелаFezai, Radhia, Majdi Mansouri, Nasreddine Bouguila, Hazem Nounou, and Mohamed Nounou. "Reduced Gaussian process regression for fault detection of chemical processes." In 2019 International Conference on Internet of Things, Embedded Systems and Communications (IINTEC). IEEE, 2019. http://dx.doi.org/10.1109/iintec48298.2019.9112136.
Повний текст джерелаLiu, Yanzhu, Fan Wang, and Wai-Kin Adams Kong. "Probabilistic Deep Ordinal Regression Based on Gaussian Processes." In 2019 IEEE/CVF International Conference on Computer Vision (ICCV). IEEE, 2019. http://dx.doi.org/10.1109/iccv.2019.00540.
Повний текст джерелаLuo, Chen, and Shiliang Sun. "Variational Mixtures of Gaussian Processes for Classification." In Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/642.
Повний текст джерелаHong, Xiaodan, Lihong Ren, Lei Chen, Fan Guo, Yongsheng Ding, and Biao Huang. "A weighted Gaussian process regression for multivariate modelling." In 2017 6th International Symposium on Advanced Control of Industrial Processes (AdCONIP). IEEE, 2017. http://dx.doi.org/10.1109/adconip.2017.7983779.
Повний текст джерелаPFINGSTL, SIMON, CHRISTIAN BRAUN, and MARKUS ZIMMERMANN. "WARPED GAUSSIAN PROCESSES FOR PROGNOSTIC HEALTH MONITORING." In Structural Health Monitoring 2021. Destech Publications, Inc., 2022. http://dx.doi.org/10.12783/shm2021/36358.
Повний текст джерелаZHOU, YATONG, TAIYI ZHANG, and ZHAOGAN LU. "A NOVEL GAUSSIAN PROCESSES MODEL FOR REGRESSION AND PREDICTION." In Proceedings of the 7th International FLINS Conference. WORLD SCIENTIFIC, 2006. http://dx.doi.org/10.1142/9789812774118_0025.
Повний текст джерелаЗвіти організацій з теми "Gaussian Regression Processes"
Shin, Tony. Gaussian process regression for radiological contamination mapping. Office of Scientific and Technical Information (OSTI), January 2021. http://dx.doi.org/10.2172/1760555.
Повний текст джерелаSchneider, M., G. Chapline, M. Otten, and C. Miller. Gaussian Process Regression as a Riemann-Hilbert Problem. Office of Scientific and Technical Information (OSTI), September 2021. http://dx.doi.org/10.2172/1828667.
Повний текст джерелаFranzman, J., and C. Kamath. Understanding the Effects of Tapering on Gaussian Process Regression. Office of Scientific and Technical Information (OSTI), August 2019. http://dx.doi.org/10.2172/1558874.
Повний текст джерелаBilionis, Ilias, and Nicholas Zabaras. Multi-output Local Gaussian Process Regression: Applications to Uncertainty Quantification. Fort Belvoir, VA: Defense Technical Information Center, December 2011. http://dx.doi.org/10.21236/ada554929.
Повний текст джерелаShin, Tony. Gaussian process regression for radiological contamination mapping Applied to optimal motion planning for mobile sensor platforms. Office of Scientific and Technical Information (OSTI), September 2021. http://dx.doi.org/10.2172/1822694.
Повний текст джерела