Relevant bibliographies by topics / Gaussian Regression Processes

Academic literature on the topic 'Gaussian Regression Processes'

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Published: 10 December 2022
Last updated: 29 January 2023

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Journal articles on the topic "Gaussian Regression Processes"

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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.

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Munoz-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.

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Terry, 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.

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Gaussian processes offer a flexible kernel method for regression. While Gaussian processes have many useful theoretical properties and have proven practically useful, they suffer from poor scaling in the number of observations. In particular, the cubic time complexity of updating standard Gaussian process models can be a limiting factor in applications. We propose an algorithm for sequentially partitioning the input space and fitting a localized Gaussian process to each disjoint region. The algorithm is shown to have superior time and space complexity to existing methods, and its sequential nature allows the model to be updated efficiently. The algorithm constructs a model for which the time complexity of updating is tightly bounded above by a pre-specified parameter. To the best of our knowledge, the model is the first local Gaussian process regression model to achieve linear memory complexity. Theoretical continuity properties of the model are proven. We demonstrate the efficacy of the resulting model on several multi-dimensional regression tasks.
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Wu, 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.

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Gaussian processes is a kind of machine learning method developed in recent years and also a promising technology that has been applied both in the regression problem and the classification problem. In this paper, the general principle of regression and classification based on Gaussian process and experimental verification was described. A comparison about accuracy between this method and Support Vector Machine (SVM) is made during the experiments.Finally, it was summarized of the regression and classification of Gaussian process application and future development direction.
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Gonçalves, 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.

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Perez-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.

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Zhang, 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.

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Gaussian processes have been widely applied to regression problems with good performance. However, they can be computationally expensive. In order to reduce the computational cost, there have been recent studies on using sparse approximations in gaussian processes. In this article, we investigate properties of certain sparse regression algorithms that approximately solve a gaussian process. We obtain approximation bounds and compare our results with related methods.
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Carvalho, 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.

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Munoz-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.

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Leithead, 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.

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Dissertations / Theses on the topic "Gaussian Regression Processes"

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Beck, Daniel Emilio. "Gaussian processes for text regression." Thesis, University of Sheffield, 2017. http://etheses.whiterose.ac.uk/17619/.

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Text Regression is the task of modelling and predicting numerical indicators or response variables from textual data. It arises in a range of different problems, from sentiment and emotion analysis to text-based forecasting. Most models in the literature apply simple text representations such as bag-of-words and predict response variables in the form of point estimates. These simplifying assumptions ignore important information coming from the data such as the underlying uncertainty present in the outputs and the linguistic structure in the textual inputs. The former is particularly important when the response variables come from human annotations while the latter can capture linguistic phenomena that go beyond simple lexical properties of a text. In this thesis our aim is to advance the state-of-the-art in Text Regression by improving these two aspects, better uncertainty modelling in the response variables and improved text representations. Our main workhorse to achieve these goals is Gaussian Processes (GPs), a Bayesian kernelised probabilistic framework. GP-based regression models the response variables as well-calibrated probability distributions, providing additional information in predictions which in turn can improve subsequent decision making. They also model the data using kernels, enabling richer representations based on similarity measures between texts. To be able to reach our main goals we propose new kernels for text which aim at capturing richer linguistic information. These kernels are then parameterised and learned from the data using efficient model selection procedures that are enabled by the GP framework. Finally we also capitalise on recent advances in the GP literature to better capture uncertainty in the response variables, such as multi-task learning and models that can incorporate non-Gaussian variables through the use of warping functions. Our proposed architectures are benchmarked in two Text Regression applications: Emotion Analysis and Machine Translation Quality Estimation. Overall we are able to obtain better results compared to baselines while also providing uncertainty estimates for predictions in the form of posterior distributions. Furthermore we show how these models can be probed to obtain insights about the relation between the data and the response variables and also how to apply predictive distributions in subsequent decision making procedures.
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Gibbs, 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.

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Bayesian inference offers us a powerful tool with which to tackle the problem of data modelling. However, the performance of Bayesian methods is crucially dependent on being able to find good models for our data. The principal focus of this thesis is the development of models based on Gaussian process priors. Such models, which can be thought of as the infinite extension of several existing finite models, have the flexibility to model complex phenomena while being mathematically simple. In this thesis, I present a review of the theory of Gaussian processes and their covariance functions and demonstrate how they fit into the Bayesian framework. The efficient implementation of a Gaussian process is discussed with particular reference to approximate methods for matrix inversion based on the work of Skilling (1993). Several regression problems are examined. Non-stationary covariance functions are developed for the regression of neuron spike data and the use of Gaussian processes to model the potential energy surfaces of weakly bound molecules is discussed. Classification methods based on Gaussian processes are implemented using variational methods. Existing bounds (Jaakkola and Jordan 1996) for the sigmoid function are used to tackle binary problems and multi-dimensional bounds on the softmax function are presented for the multiple class case. The performance of the variational classifier is compared with that of other methods using the CRABS and PIMA datasets (Ripley 1996) and the problem of predicting the cracking of welds based on their chemical composition is also investigated. The theoretical calculation of the density of states of crystal structures is discussed in detail. Three possible approaches to the problem are described based on free energy minimization, Gaussian processes and the theory of random matrices. Results from these approaches are compared with the state-of-the-art techniques (Pickard 1997).
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Wågberg, 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.

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The topic of this thesis is occupancy mapping for mobile robots, with an emphasis on a novel method for continuous occupancy mapping using Gaussian processes. In the new method, spatial correlation is accounted for in a natural way, and an a priori discretization of the area to be mapped is not necessary as within most other common methods. The main contribution of this thesis is the construction of a Gaussian process library for C++, and the use of this library to implement the continuous occupancy mapping algorithm. The continuous occupancy mapping is evaluated using both simulated and real world experimental data. The main result is that the method, in its current form, is not fit for online operations due to its computational complexity. By using approximations and ad hoc solutions, the method can be run in real time on a mobile robot, though not without losing many of its benefits.
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Davies, 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.

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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.

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Rasmussen, 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.

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Sun, Furong. "Some Advances in Local Approximate Gaussian Processes." Diss., Virginia Tech, 2019. http://hdl.handle.net/10919/97245.

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Nowadays, Gaussian Process (GP) has been recognized as an indispensable statistical tool in computer experiments. Due to its computational complexity and storage demand, its application in real-world problems, especially in "big data" settings, is quite limited. Among many strategies to tailor GP to such settings, Gramacy and Apley (2015) proposed local approximate GP (laGP), which constructs approximate predictive equations by constructing small local designs around the predictive location under certain criterion. In this dissertation, several methodological extensions based upon laGP are proposed. One methodological contribution is the multilevel global/local modeling, which deploys global hyper-parameter estimates to perform local prediction. The second contribution comes from extending the laGP notion of "locale" to a set of predictive locations, along paths in the input space. These two contributions have been applied in the satellite drag emulation, which is illustrated in Chapter 3. Furthermore, the multilevel GP modeling strategy has also been applied to synthesize field data and computer model outputs of solar irradiance across the continental United States, combined with inverse-variance weighting, which is detailed in Chapter 4. Last but not least, in Chapter 5, laGP's performance has been tested on emulating daytime land surface temperatures estimated via satellites, in the settings of irregular grid locations.
Doctor 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.
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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.

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Les simulations par ordinateur sont un outil de grande importance pour les mathématiciens appliqués et les ingénieurs. Elles sont devenues plus précises mais aussi plus compliquées. Tellement compliquées, que le temps de lancement par calcul est prohibitif. Donc, plusieurs aspects de ces simulations sont mal compris. Par exemple, souvent ces simulations dépendent des paramètres qu'ont une valeur inconnue.Un metamodèle est une reconstruction de la simulation. Il produit des réponses proches à celles de la simulation avec un temps de calcul très réduit. Avec ce metamodèle il est possible d'étudier certains aspects de la simulation. Il est construit avec peu de données et son objectif est de remplacer la simulation originale.Ce travail est concerné avec la construction des metamodèles dans un cadre particulier appelé multi-fidélité. En multi-fidélité, le metamodèle est construit à partir des données produites par une simulation objective et des données qu'ont une relation avec cette simulation. Ces données approximées peuvent être générés par des versions dégradées de la simulation ; par des anciennes versions qu'ont été largement étudiées ou par une autre simulation dans laquelle une partie de la description est simplifiée.En apprenant la différence entre les données il est possible d'incorporer l'information approximée et ce ci peut nous conduire vers un metamodèle amélioré. Deux approches pour atteindre ce but sont décrites dans ce manuscrit : la première est basée sur des modèles avec des processus gaussiens et la seconde sur une décomposition à base d'ondelettes. La première montre qu'en estimant la relation il est possible d'incorporer des données qui n'ont pas de valeur autrement. Dans la seconde, les données sont ajoutées de façon adaptative pour améliorer le metamodèle.L'objet de ce travail est d'améliorer notre compréhension sur comment incorporer des données approximées pour produire des metamodèles plus précis. Travailler avec un metamodèle multi-fidélité nous aide à comprendre en détail ces éléments. A la fin une image globale des parties qui forment ce metamodèle commence à s'esquisser : les relations et différences entres les données deviennent plus claires
A 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
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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.

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Data-driven prediction of battery health has gained increased attention over the past couple of years, in both academia and industry. Accurate early-stage predictions of battery performance would create new opportunities regarding production and use. Using data from only the first 100 cycles, in a data set of 124 cells where lifetimes span between 150 and 2300 cycles, this work combines parametric linear models with non-parametric Gaussian process regression to achieve cycle lifetime predictions with an overall accuracy of 8.8% mean error. This work presents a relevant contribution to current research as this combination of methods is previously unseen when regressing battery lifetime on a high dimensional feature space. The study and the results presented further show that Gaussian process regression can serve as a valuable contributor in future data-driven implementations of battery health predictions.
Datadriven 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.
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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.

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In the modern society it is well known that an individual’s background matters in his career, but should it be taken into consideration in a recruiting process in general and a recruiting process of NBA-players in particular? Previous research shows that white basketball players from high-income families have a 75% higher chance of becoming an NBA player compared to a white basketball player from a low-income family. In this paper, we have examined whether there is a connection between NBA-player background and the chances of succeeding in the NBA given that the player has been picked in the NBA-draft. The results have been carried out using machine learning algorithms based on Gaussian Processes. The results show that draft decisions will not be improved by taking socio-economic background into consideration.
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Books on the topic "Gaussian Regression Processes"

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Taeryon, Choi, ed. Gaussian process regression analysis for functional data. Boca Raton, FL: CRC Press, 2011.

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Rasmussen, Carl Edward. Evaluation of Gaussian processes and other methods for non-linear regression. Toronto: University of Toronto, Dept. of Computer Science, 1997.

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Neal, Radford M. Monte Carlo implementation of Gaussian process models for Bayesian regression and classification. Toronto: University of Toronto, 1997.

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Applied parameter estimation for chemical engineers. New York: Marcel Dekker, 2001.

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Shi, Jian Qing, and Taeryon Choi. Gaussian Process Regression Analysis for Functional Data. Taylor & Francis Group, 2011.

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Shi, Jian Qing, and Taeryon Choi. Gaussian Process Regression Analysis for Functional Data. Taylor & Francis Group, 2011.

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Shi, Jian Qing, and Taeryon Choi. Gaussian Process Regression Analysis for Functional Data. Taylor & Francis Group, 2011.

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Faraway, Julian J., Xiaofeng Wang, and Yu Ryan Yue. Bayesian Regression Modeling with INLA. Taylor & Francis Group, 2018.

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Faraway, Julian J., Xiaofeng Wang, and Yu Ryan Yue. Bayesian Regression Modeling with INLA. Taylor & Francis Group, 2018.

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Bayesian Regression Modeling with INLA. Taylor & Francis Group, 2018.

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Book chapters on the topic "Gaussian Regression Processes"

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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.

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Dixon, 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.

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Luschgy, 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.

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Srijith, 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.

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Gao, 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.

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Sudhaman, 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.

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Pilon, 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.

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Qiang, 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.

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Williams, 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.

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Antunes, 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.

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Conference papers on the topic "Gaussian Regression Processes"

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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.

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Guerrero, 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.

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Le, 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.

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Echeverria 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.

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Fezai, 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.

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Liu, 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.

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Luo, 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.

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Gaussian Processes (GPs) are powerful tools for machine learning which have been applied to both classification and regression. The mixture models of GPs were later proposed to further improve GPs for data modeling. However, these models are formulated for regression problems. In this work, we propose a new Mixture of Gaussian Processes for Classification (MGPC). Instead of the Gaussian likelihood for regression, MGPC employs the logistic function as likelihood to obtain the class probabilities, which is suitable for classification problems. The posterior distribution of latent variables is approximated through variational inference. The hyperparameters are optimized through the variational EM method and a greedy algorithm. Experiments are performed on multiple real-world datasets which show improvements over five widely used methods on predictive performance. The results also indicate that for classification MGPC is significantly better than the regression model with mixtures of GPs, different from the existing consensus that their single model counterparts are comparable.
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Hong, 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.

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PFINGSTL, 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.

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Gaussian process regression is a powerful method for predicting states associated with uncertainty. A common application field is to predict damage states of structural systems. Recently, Gaussian processes became very popular as they deliver credible intervals for the predicted states. However, one major disadvantage of Gaussian processes is that they assume a normal distribution. This is not justified when the relevant variables can only assume positive values, such as crack lengths or damage states. This paper presents a way to bypass this problem by using warped Gaussian processes: We (1) transform the data with a warping function, (2) apply Gaussian process regression in the latent space, and (3) transform the results back by using the inverse of the warping function. The method is applied to a crack growth example. The paper shows how to integrate prior knowledge into warped Gaussian processes in order to increase prediction accuracy and that warped Gaussian processes lead to better and more plausible results.
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ZHOU, 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.

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Reports on the topic "Gaussian Regression Processes"

1

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.

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Schneider, 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.

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Franzman, 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.

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Bilionis, 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.

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Shin, 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.

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