Relevant bibliographies by topics / Gaussian process regression model

Academic literature on the topic 'Gaussian process regression model'

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Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Gaussian process regression model"

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Sofro, A., and A. Oktaviarina. "Gaussian Process Regression Model in Spatial Logistic Regression." Journal of Physics: Conference Series 947 (January 2018): 012005. http://dx.doi.org/10.1088/1742-6596/947/1/012005.

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Nguyen-Tuong, Duy, Matthias Seeger, and Jan Peters. "Model Learning with Local Gaussian Process Regression." Advanced Robotics 23, no. 15 (January 2009): 2015–34. http://dx.doi.org/10.1163/016918609x12529286896877.

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Wang, Bo, and Jian Qing Shi. "Generalized Gaussian Process Regression Model for Non-Gaussian Functional Data." Journal of the American Statistical Association 109, no. 507 (July 3, 2014): 1123–33. http://dx.doi.org/10.1080/01621459.2014.889021.

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Pearce, Robert, Peter Ireland, and Eduardo Romero. "Thermal matching using Gaussian process regression." Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering 234, no. 6 (January 28, 2020): 1172–80. http://dx.doi.org/10.1177/0954410020901961.

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Thermal matching is a key stage of the development process for a gas turbine engine where component models are verified to ensure the correct metal temperature distribution has been used in life calculations. The thermal match involves adjusting parameters of a thermal model in order to match an experimental temperature distribution, usually obtained from a thermal paint test. Current methodologies involve manually adjusting parameters, which is both time consuming and leads to variation in the matches achieved. This paper presents a new method to conduct thermal matching, where Gaussian process regression is utilised to obtain a surrogate model from which optimal parameters for matching are obtained. This standardised procedure removes subjectivity from the match and gives faster and more consistent matches. The method is introduced and demonstrated for a number of cases involving a leading edge impingement system that has been isolated from a high pressure turbine blade.
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Park, Chiwoo, David J. Borth, Nicholas S. Wilson, Chad N. Hunter, and Fritz J. Friedersdorf. "Robust Gaussian process regression with a bias model." Pattern Recognition 124 (April 2022): 108444. http://dx.doi.org/10.1016/j.patcog.2021.108444.

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Hewing, Lukas, Juraj Kabzan, and Melanie N. Zeilinger. "Cautious Model Predictive Control Using Gaussian Process Regression." IEEE Transactions on Control Systems Technology 28, no. 6 (November 2020): 2736–43. http://dx.doi.org/10.1109/tcst.2019.2949757.

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Bachoc, Francois, Fabrice Gamboa, Jean-Michel Loubes, and Nil Venet. "A Gaussian Process Regression Model for Distribution Inputs." IEEE Transactions on Information Theory 64, no. 10 (October 2018): 6620–37. http://dx.doi.org/10.1109/tit.2017.2762322.

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Misumi, Toshihiro. "MODEL SELECTION FOR FUNCTIONAL MIXED MODEL VIA GAUSSIAN PROCESS REGRESSION." Bulletin of informatics and cybernetics 46 (December 2014): 23–35. http://dx.doi.org/10.5109/1798144.

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Chapaneri, Santosh, and Deepak Jayaswal. "Structured Gaussian Process Regression of Music Mood." Fundamenta Informaticae 176, no. 2 (December 18, 2020): 183–203. http://dx.doi.org/10.3233/fi-2020-1970.

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Modeling the music mood has wide applications in music categorization, retrieval, and recommendation systems; however, it is challenging to computationally model the affective content of music due to its subjective nature. In this work, a structured regression framework is proposed to model the valence and arousal mood dimensions of music using a single regression model at a linear computational cost. To tackle the subjectivity phenomena, a confidence-interval based estimated consensus is computed by modeling the behavior of various annotators (e.g. biased, adversarial) and is shown to perform better than using the average annotation values. For a compact feature representation of music clips, variational Bayesian inference is used to learn the Gaussian mixture model representation of acoustic features and chord-related features are used to improve the valence estimation by probing the chord progressions between chroma frames. The dimensionality of features is further reduced using an adaptive version of kernel PCA. Using an efficient implementation of twin Gaussian process for structured regression, the proposed work achieves a significant improvement in R2 for arousal and valence dimensions relative to state-of-the-art techniques on two benchmark datasets for music mood estimation.
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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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Dissertations / Theses on the topic "Gaussian process regression model"

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Srinivasan, Balaji Vasan. "Gaussian process regression for model estimation." College Park, Md.: University of Maryland, 2008. http://hdl.handle.net/1903/8962.

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Thesis (M.S.) -- University of Maryland, College Park, 2008.
Thesis research directed by: Dept. of Electrical and Computer Engineering E. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.
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Sofro, A'yunin. "Convolved Gaussian process regression models for multivariate non-Gaussian data." Thesis, University of Newcastle upon Tyne, 2017. http://hdl.handle.net/10443/3723.

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Multivariate regression analysis has been developed rapidly in the last decade for dependent data. The most di cult part in multivariate cases is how to construct a crosscorrelation between response variables. We need to make sure that the covariance matrix is positive de nite which is not an easy task. Several approaches have been developed to overcome the issue. However, most of them have some limitations, such as it is hard to extend it to the case involving high dimensional variables or capture individual characteristics. It also should point out that the meaning of the cross-correlation structure for some methods is unclear. To address the issues, we propose to use convolved Gaussian process (CGP) priors (Boyle & Frean, 2005). In this dissertation, we propose a novel approach for multivariate regression using CGP priors. The approach provides a semiparametric model with multi-dimensional covariates and o ers a natural framework for modelling common mean structures and covariance structures simultaneously for multivariate dependent data. Information about observations is provided by the common mean structure while individual characteristics also can be captured by the covariance structure. At the same time, the covariance function is able to accommodate a large-dimensional covariate as well. We start to make a broader problem from a general framework of CGP proposed by Andriluka et al. (2006). We investigate some of the stationary covariance functions and the mixed forms for constructing multiple dependent Gaussian processes to solve a more complex issue. Then, we extend the idea to a multivariate non-linear regression model by using convolved Gaussian processes as priors. We then focus on an applying the idea to multivariate non-Gaussian data, i.e. multivariate Poisson, and other multivariate non-Gaussian distributions from the exponential family. We start our focus on multivariate Poisson data which are found in many problems relating to public health issues. Then nally, we provide a general framework for a multivariate binomial data and other multivariate non-Gaussian data. The de nition of the model, the inference, and the implementation, as well as its asymptotic properties, are discussed. Comprehensive numerical examples with both simulation studies and real data are presented.
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Yi, Gang. "Variable Selection with Penalized Gaussian Process Regression Models." Thesis, University of Newcastle upon Tyne, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.515061.

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Nguyen, Huong. "Near-optimal designs for Gaussian Process regression models." The Ohio State University, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=osu1533983585774383.

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Erich, Roger Alan. "Regression Modeling of Time to Event Data Using the Ornstein-Uhlenbeck Process." The Ohio State University, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=osu1342796812.

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Tietze, Nils [Verfasser], Ulrich [Akademischer Betreuer] Konigorski, and Oliver [Akademischer Betreuer] Nelles. "Model-based Calibration of Engine Control Units Using Gaussian Process Regression / Nils Tietze. Betreuer: Ulrich Konigorski ; Oliver Nelles." Darmstadt : Universitäts- und Landesbibliothek Darmstadt, 2015. http://d-nb.info/1111909903/34.

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Barrett, James Edward. "Gaussian process regression models for the analysis of survival data with competing risks, interval censoring and high dimensionality." Thesis, King's College London (University of London), 2015. http://kclpure.kcl.ac.uk/portal/en/theses/gaussian-process-regression-models-for-the-analysis-of-survival-data-with-competing-risks-interval-censoring-and-high-dimensionality(fe3440e1-9766-4fc3-9d23-fe4af89483b5).html.

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We develop novel statistical methods for analysing biomedical survival data based on Gaussian process (GP) regression. GP regression provides a powerful non-parametric probabilistic method of relating inputs to outputs. We apply this to survival data which consist of time-to-event and covariate measurements. In the context of GP regression the covariates are regarded as `inputs' and the event times are the `outputs'. This allows for highly exible inference of non-linear relationships between covariates and event times. Many existing methods for analysing survival data, such as the ubiquitous Cox proportional hazards model, focus primarily on the hazard rate which is typically assumed to take some parametric or semi-parametric form. Our proposed model belongs to the class of accelerated failure time models and as such our focus is on directly characterising the relationship between the covariates and event times without any explicit assumptions on what form the hazard rates take. This provides a more direct route to connecting the covariates to survival outcomes with minimal assumptions. An application of our model to experimental data illustrates its usefulness. We then apply multiple output GP regression, which can handle multiple potentially correlated outputs for each input, to competing risks survival data where multiple event types can occur. In this case the multiple outputs correspond to the time-to-event for each risk. By tuning one of the model parameters we can control the extent to which the multiple outputs are dependent thus allowing the specication of correlated risks. However, the identiability problem, which states that it is not possible to infer whether risks are truly independent or otherwise on the basis of observed data, still holds. In spite of this fundamental limitation simulation studies suggest that in some cases assuming dependence can lead to more accurate predictions. The second part of this thesis is concerned with high dimensional survival data where there are a large number of covariates compared to relatively few individuals. This leads to the problem of overtting, where spurious relationships are inferred from the data. One strategy to tackle this problem is dimensionality reduction. The Gaussian process latent variable model (GPLVM) is a powerful method of extracting a low dimensional representation of high dimensional data. We extend the GPLVM to incorporate survival outcomes by combining the model with a Weibull proportional hazards model (WPHM). By reducing the ratio of covariates to samples we hope to diminish the eects of overtting. The combined GPLVM-WPHM model can also be used to combine several datasets by simultaneously expressing them in terms of the same low dimensional latent variables. We construct the Laplace approximation of the marginal likelihood and use this to determine the optimal number of latent variables, thereby allowing detection of intrinsic low dimensional structure. Results from both simulated and real data show a reduction in overtting and an increase in predictive accuracy after dimensionality reduction.
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Xu, Li. "Statistical Methods for Variability Management in High-Performance Computing." Diss., Virginia Tech, 2021. http://hdl.handle.net/10919/104184.

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High-performance computing (HPC) variability management is an important topic in computer science. Research topics include experimental designs for efficient data collection, surrogate models for predicting the performance variability, and system configuration optimization. Due to the complex architecture of HPC systems, a comprehensive study of HPC variability needs large-scale datasets, and experimental design techniques are useful for improved data collection. Surrogate models are essential to understand the variability as a function of system parameters, which can be obtained by mathematical and statistical models. After predicting the variability, optimization tools are needed for future system designs. This dissertation focuses on HPC input/output (I/O) variability through three main chapters. After the general introduction in Chapter 1, Chapter 2 focuses on the prediction models for the scalar description of I/O variability. A comprehensive comparison study is conducted, and major surrogate models for computer experiments are investigated. In addition, a tool is developed for system configuration optimization based on the chosen surrogate model. Chapter 3 conducts a detailed study for the multimodal phenomena in I/O throughput distribution and proposes an uncertainty estimation method for the optimal number of runs for future experiments. Mixture models are used to identify the number of modes for throughput distributions at different configurations. This chapter also addresses the uncertainty in parameter estimation and derives a formula for sample size calculation. The developed method is then applied to HPC variability data. Chapter 4 focuses on the prediction of functional outcomes with both qualitative and quantitative factors. Instead of a scalar description of I/O variability, the distribution of I/O throughput provides a comprehensive description of I/O variability. We develop a modified Gaussian process for functional prediction and apply the developed method to the large-scale HPC I/O variability data. Chapter 5 contains some general conclusions and areas for future work.
Doctor of Philosophy
This dissertation focuses on three projects that are all related to statistical methods in performance variability management in high-performance computing (HPC). HPC systems are computer systems that create high performance by aggregating a large number of computing units. The performance of HPC is measured by the throughput of a benchmark called the IOZone Filesystem Benchmark. The performance variability is the variation among throughputs when the system configuration is fixed. Variability management involves studying the relationship between performance variability and the system configuration. In Chapter 2, we use several existing prediction models to predict the standard deviation of throughputs given different system configurations and compare the accuracy of predictions. We also conduct HPC system optimization using the chosen prediction model as the objective function. In Chapter 3, we use the mixture model to determine the number of modes in the distribution of throughput under different system configurations. In addition, we develop a model to determine the number of additional runs for future benchmark experiments. In Chapter 4, we develop a statistical model that can predict the throughout distributions given the system configurations. We also compare the prediction of summary statistics of the throughput distributions with existing prediction models.
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Edwards, Adam Michael. "Precision Aggregated Local Models." Diss., Virginia Tech, 2021. http://hdl.handle.net/10919/102125.

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Large scale Gaussian process (GP) regression is infeasible for larger data sets due to cubic scaling of flops and quadratic storage involved in working with covariance matrices. Remedies in recent literature focus on divide-and-conquer, e.g., partitioning into sub-problems and inducing functional (and thus computational) independence. Such approximations can speedy, accurate, and sometimes even more flexible than an ordinary GPs. However, a big downside is loss of continuity at partition boundaries. Modern methods like local approximate GPs (LAGPs) imply effectively infinite partitioning and are thus pathologically good and bad in this regard. Model averaging, an alternative to divide-and-conquer, can maintain absolute continuity but often over-smooth, diminishing accuracy. Here I propose putting LAGP-like methods into a local experts-like framework, blending partition-based speed with model-averaging continuity, as a flagship example of what I call precision aggregated local models (PALM). Using N_C LAGPs, each selecting n from N data pairs, I illustrate a scheme that is at most cubic in n, quadratic in N_C, and linear in N, drastically reducing computational and storage demands. Extensive empirical illustration shows how PALM is at least as accurate as LAGP, can be much faster in terms of speed, and furnishes continuous predictive surfaces. Finally, I propose sequential updating scheme which greedily refines a PALM predictor up to a computational budget, and several variations on the basic PALM that may provide predictive improvements.
Doctor of Philosophy
Occasionally, when describing the relationship between two variables, it may be helpful to use a so-called ``non-parametric" regression that is agnostic to the function that connects them. Gaussian Processes (GPs) are a popular method of non-parametric regression used for their relative flexibility and interpretability, but they have the unfortunate drawback of being computationally infeasible for large data sets. Past work into solving the scaling issues for GPs has focused on ``divide and conquer" style schemes that spread the data out across multiple smaller GP models. While these model make GP methods much more accessible to large data sets they do so either at the expense of local predictive accuracy of global surface continuity. Precision Aggregated Local Models (PALM) is a novel divide and conquer method for GP models that is scalable for large data while maintaining local accuracy and a smooth global model. I demonstrate that PALM can be built quickly, and performs well predictively compared to other state of the art methods. This document also provides a sequential algorithm for selecting the location of each local model, and variations on the basic PALM methodology.
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Chu, Shuyu. "Change Detection and Analysis of Data with Heterogeneous Structures." Diss., Virginia Tech, 2017. http://hdl.handle.net/10919/78613.

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Heterogeneous data with different characteristics are ubiquitous in the modern digital world. For example, the observations collected from a process may change on its mean or variance. In numerous applications, data are often of mixed types including both discrete and continuous variables. Heterogeneity also commonly arises in data when underlying models vary across different segments. Besides, the underlying pattern of data may change in different dimensions, such as in time and space. The diversity of heterogeneous data structures makes statistical modeling and analysis challenging. Detection of change-points in heterogeneous data has attracted great attention from a variety of application areas, such as quality control in manufacturing, protest event detection in social science, purchase likelihood prediction in business analytics, and organ state change in the biomedical engineering. However, due to the extraordinary diversity of the heterogeneous data structures and complexity of the underlying dynamic patterns, the change-detection and analysis of such data is quite challenging. This dissertation aims to develop novel statistical modeling methodologies to analyze four types of heterogeneous data and to find change-points efficiently. The proposed approaches have been applied to solve real-world problems and can be potentially applied to a broad range of areas.
Ph. D.
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Books on the topic "Gaussian process regression model"

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

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Bera, Anil K. Specification test for a linear regression model with arch process. Champaign: University of Illinois at Urbana-Champaign, 1993.

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

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Lee, Herbert K. H., Matthew Taddy, Robert Gramacy, and Genetha Gray. Designing and analysing a circuit device experiment using treed Gaussian processes. Edited by Anthony O'Hagan and Mike West. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780198703174.013.28.

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This article describes a new circuit device, developed in collaboration with scientists at Sandia National Laboratories, based on treed Gaussian processes (TGP). The circuit devices under study are bipolar junction transistors, which are used to amplify electrical current. To aid with the design of the device, a computer model predicts its peak output as a function of the input dosage and a number of design parameters. The methodology also involves a novel sequential design procedure to generate data to fit the emulator. Both physical and computer simulation experiments are performed, and the results show that the TGP model can be useful for spatial data and semiparametric regression in the context of a computer experiment for designing a circuit device, for sequential design of (computer) experiments, sequential robust local optimization, validation, calibration, and sensitivity analysis.
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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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Liu, Peter Junteng. Using Gaussian process regression to denoise images and remove artefacts from microarray data. 2007.

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Vidales, A. MACHINE LEARNING with MATLAB: GAUSSIAN PROCESS REGRESSION, ANALYSIS of VARIANCE and BAYESIAN OPTIMIZATION. Independently Published, 2019.

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Book chapters on the topic "Gaussian process regression model"

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Gorbach, Nico S., Andrew An Bian, Benjamin Fischer, Stefan Bauer, and Joachim M. Buhmann. "Model Selection for Gaussian Process Regression." In Lecture Notes in Computer Science, 306–18. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-66709-6_25.

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Le, Quoc V., Alex J. Smola, Thomas Gärtner, and Yasemin Altun. "Transductive Gaussian Process Regression with Automatic Model Selection." In Lecture Notes in Computer Science, 306–17. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11871842_31.

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Mohammed, Rekar O., and Gavin C. Cawley. "Over-Fitting in Model Selection with Gaussian Process Regression." In Machine Learning and Data Mining in Pattern Recognition, 192–205. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-62416-7_14.

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Sollich, Peter. "Can Gaussian Process Regression Be Made Robust Against Model Mismatch?" In Lecture Notes in Computer Science, 199–210. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11559887_12.

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Zhikun, He, Liu Guangbin, Zhao Xijing, and Yang Jian. "Temperature Model for FOG Zero-Bias Using Gaussian Process Regression." In Intelligence Computation and Evolutionary Computation, 37–45. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-31656-2_6.

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Wickramarachchi, Chandula T., Timothy J. Rogers, Wayne Leahy, and Elizabeth J. Cross. "Predicting Tool Wear Using Linear Response Surface Methodology and Gaussian Process Regression." In Topics in Modal Analysis & Testing, Volume 8, 283–86. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-47717-2_29.

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Gibson, Samuel J., Timothy J. Rogers, and Elizabeth J. Cross. "Integrating Physical Knowledge into Gaussian Process Regression Models for Probabilistic Fatigue Assessment." In Lecture Notes in Civil Engineering, 472–81. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-07322-9_48.

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Mohd Aris, Muhammad Naeim, Hanita Daud, Khairul Arifin Mohd Noh, and Sarat Chandra Dass. "Estimating Marine CSEM Responses Using Gaussian Process Regression Based on Synthetic Models." In Studies in Systems, Decision and Control, 235–47. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-79606-8_17.

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Ferroudji, Karim, Abdelmaled Reddaf, Islem Bouchachi, and Boudjerda Mounir. "A Model Selection Strategy of Gaussian Process Regression for Modeling Inset-Fed Microstrip Patch Antenna." In Lecture Notes in Electrical Engineering, 75–87. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-0408-8_7.

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Lam, Max W. Y. "TLGProb: Two-Layer Gaussian Process Regression Model for Winning Probability Calculation in Two-Team Sports." In Artificial Intelligence and Soft Computing, 280–91. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-59060-8_26.

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Conference papers on the topic "Gaussian process regression model"

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Li, Shibo, Wei Xing, Robert M. Kirby, and Shandian Zhe. "Scalable Gaussian Process Regression Networks." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/340.

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Gaussian process regression networks (GPRN) are powerful Bayesian models for multi-output regression, but their inference is intractable. To address this issue, existing methods use a fully factorized structure (or a mixture of such structures) over all the outputs and latent functions for posterior approximation, which, however, can miss the strong posterior dependencies among the latent variables and hurt the inference quality. In addition, the updates of the variational parameters are inefficient and can be prohibitively expensive for a large number of outputs. To overcome these limitations, we propose a scalable variational inference algorithm for GPRN, which not only captures the abundant posterior dependencies but also is much more efficient for massive outputs. We tensorize the output space and introduce tensor/matrix-normal variational posteriors to capture the posterior correlations and to reduce the parameters. We jointly optimize all the parameters and exploit the inherent Kronecker product structure in the variational model evidence lower bound to accelerate the computation. We demonstrate the advantages of our method in several real-world applications.
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Lv, Jiapeng, and Xianjun Shi. "Battery Degradation Prediction Model Based on Gaussian Process Regression." In 2019 IEEE 3rd Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC). IEEE, 2019. http://dx.doi.org/10.1109/imcec46724.2019.8984028.

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Bu, Xingang, Hassan Saleh, Ming Han, and Abdulkareem AlSofi. "Permeability Prediction of Carbonate Cores With Gaussian Process Regression Model." In SPE Reservoir Characterisation and Simulation Conference and Exhibition. SPE, 2023. http://dx.doi.org/10.2118/212592-ms.

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Abstract Machine leaning (ML) methods are widely adopted in predictions affected by various factors. This paper presents a step-by-step workflow of applying a ML approach to develop a heterogeneous permeability prediction model from the CT images of core samples. In this work, over ten thousand 3-D sub-image were randomly extracted from the CT images of two heterogeneous carbonate core samples. The permeability of each sub-image is simulated using pore network modeling (PNM) method. Ten features including porosity, pore size, surface area, specific surface area and connection coefficient etc. are extracted from sub-image by a statistical method. Three training datasets were built with features and permeability. Each set of training data is input into a ML model pool, which contains 19 regression models of 5 types including linear regression models, regression trees, support vector machines, Gaussian process regression models and ensembles of trees. Then, regression models are trained to identify the one that can yield the best permeability prediction. The trained model with the highest R-Squared value is selected for permeability prediction from binary CT images. Overall, comparing the training outputs indicate that Gaussian Process Regression models (GPR) correlate features and permeability well. For the tested heterogeneous core plugs, the exponential Gaussian Process model performs the best. The R-Squared values of the three sets of training data are 0.88, 0.87 and 0.91 respectively. Afterwards, the selected ML model was tested with additional data, and the R-squared value of each test dataset was greater than 0.85, confirming a strong predictive performance. The trained model based on ML method eliminates the conventional time-consuming operations including distance transformation and watershed segmentation. It also avoids excessive memory consumption, which makes the method suitable for images with large size. The paper provides a way to develop an alternative approach of PNM simulation method for permeability prediction from CT images.
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Inanlouganji, Alireza, Giulia Pedrielli, Georgios Fainekos, and Sebastian Pokutta. "CONTINUOUS SIMULATION OPTIMIZATION WITH MODEL MISMATCH USING GAUSSIAN PROCESS REGRESSION." In 2018 Winter Simulation Conference (WSC). IEEE, 2018. http://dx.doi.org/10.1109/wsc.2018.8632427.

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Tian, Zhuang, Dongdong Weng, Jianying Hao, Yupeng Zhang, and Dandan Meng. "A data driven BRDF model based on Gaussian process regression." In International Conference on Optical Instruments and Technology (OIT2013), edited by Yongtian Wang, Xiaocong Yuan, Yunlong Sheng, and Kimio Tatsuno. SPIE, 2013. http://dx.doi.org/10.1117/12.2036467.

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Yadav, Anamika, Ayush Kumar, Rudra Pratap Singh Rana, Maya Chandrakar, Mohammad Pazoki, and Ragab A. El Sehiemy. "An Efficient Monthly Load Forecasting Model Using Gaussian Process Regression." In 2021 IEEE 4th International Conference on Computing, Power and Communication Technologies (GUCON). IEEE, 2021. http://dx.doi.org/10.1109/gucon50781.2021.9574008.

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Fu, Zhenyu, Yuanbao Chen, Xudong Wang, and Jin Zhao. "Identification of Ship Course Model Based on Gaussian Process Regression." In 2021 China Automation Congress (CAC). IEEE, 2021. http://dx.doi.org/10.1109/cac53003.2021.9728142.

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Najmon, Joel C., Homero Valladares, and Andres Tovar. "Multiscale Topology Optimization With Gaussian Process Regression Models." 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/detc2021-66758.

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Abstract Multiscale topology optimization (MSTO) is a numerical design approach to optimally distribute material within coupled design domains at multiple length scales. Due to the substantial computational cost of performing topology optimization at multiple scales, MSTO methods often feature subroutines such as homogenization of parameterized unit cells and inverse homogenization of periodic microstructures. Parameterized unit cells are of great practical use, but limit the design to a pre-selected cell shape. On the other hand, inverse homogenization provide a physical representation of an optimal periodic microstructure at every discrete location, but do not necessarily embody a manufacturable structure. To address these limitations, this paper introduces a Gaussian process regression model-assisted MSTO method that features the optimal distribution of material at the macroscale and topology optimization of a manufacturable microscale structure. In the proposed approach, a macroscale optimization problem is solved using a gradient-based optimizer The design variables are defined as the homogenized stiffness tensors of the microscale topologies. As such, analytical sensitivity is not possible so the sensitivity coefficients are approximated using finite differences after each microscale topology is optimized. The computational cost of optimizing each microstructure is dramatically reduced by using Gaussian process regression models to approximate the homogenized stiffness tensor. The capability of the proposed MSTO method is demonstrated with two three-dimensional numerical examples. The correlation of the Gaussian process regression models are presented along with the final multiscale topologies for the two examples: a cantilever beam and a 3-point bending beam.
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Zhang, Xiaoyu, Song Gao, Tingwei Wang, Yongqing Li, and Peng Ren. "Correcting Predictions from Simulating Wave Nearshore Model via Gaussian Process Regression." In Global Oceans 2020: Singapore - U.S. Gulf Coast. IEEE, 2020. http://dx.doi.org/10.1109/ieeeconf38699.2020.9389333.

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Ren, Rui, and Shaoyuan Li. "Enhanced Gaussian Process Regression for Active Learning Model-based Predictive Control." In 2021 40th Chinese Control Conference (CCC). IEEE, 2021. http://dx.doi.org/10.23919/ccc52363.2021.9550058.

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Reports on the topic "Gaussian process regression model"

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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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Liu, Nian, and Matthew Sweeney. Gaussian Process Emulators for Volcanic Ash Dispersion Model Tephra2. Office of Scientific and Technical Information (OSTI), July 2022. http://dx.doi.org/10.2172/1879348.

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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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Helmut, Harbrecht, John Davis Jakeman, and 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), March 2020. http://dx.doi.org/10.2172/1608084.

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Edwards, Lloyd, and Bernie Parresol. Development of a Regression Kriging Model Conditioned with Sequential Gaussian Simulation to Predict the Spatial Distribution of Site Index for The Savannah River Site. Office of Scientific and Technical Information (OSTI), September 2012. http://dx.doi.org/10.2172/1127174.

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Wegner, Michael D. Physician Provider Profiling in Brooke Army Medical Center's Internal Medicine Clinic: A Multiple Regression and Process Control Model. Fort Belvoir, VA: Defense Technical Information Center, December 1999. http://dx.doi.org/10.21236/ada420371.

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Engel, Bernard, Yael Edan, James Simon, Hanoch Pasternak, and Shimon Edelman. Neural Networks for Quality Sorting of Agricultural Produce. United States Department of Agriculture, July 1996. http://dx.doi.org/10.32747/1996.7613033.bard.

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The objectives of this project were to develop procedures and models, based on neural networks, for quality sorting of agricultural produce. Two research teams, one in Purdue University and the other in Israel, coordinated their research efforts on different aspects of each objective utilizing both melons and tomatoes as case studies. At Purdue: An expert system was developed to measure variances in human grading. Data were acquired from eight sensors: vision, two firmness sensors (destructive and nondestructive), chlorophyll from fluorescence, color sensor, electronic sniffer for odor detection, refractometer and a scale (mass). Data were analyzed and provided input for five classification models. Chlorophyll from fluorescence was found to give the best estimation for ripeness stage while the combination of machine vision and firmness from impact performed best for quality sorting. A new algorithm was developed to estimate and minimize training size for supervised classification. A new criteria was established to choose a training set such that a recurrent auto-associative memory neural network is stabilized. Moreover, this method provides for rapid and accurate updating of the classifier over growing seasons, production environments and cultivars. Different classification approaches (parametric and non-parametric) for grading were examined. Statistical methods were found to be as accurate as neural networks in grading. Classification models by voting did not enhance the classification significantly. A hybrid model that incorporated heuristic rules and either a numerical classifier or neural network was found to be superior in classification accuracy with half the required processing of solely the numerical classifier or neural network. In Israel: A multi-sensing approach utilizing non-destructive sensors was developed. Shape, color, stem identification, surface defects and bruises were measured using a color image processing system. Flavor parameters (sugar, acidity, volatiles) and ripeness were measured using a near-infrared system and an electronic sniffer. Mechanical properties were measured using three sensors: drop impact, resonance frequency and cyclic deformation. Classification algorithms for quality sorting of fruit based on multi-sensory data were developed and implemented. The algorithms included a dynamic artificial neural network, a back propagation neural network and multiple linear regression. Results indicated that classification based on multiple sensors may be applied in real-time sorting and can improve overall classification. Advanced image processing algorithms were developed for shape determination, bruise and stem identification and general color and color homogeneity. An unsupervised method was developed to extract necessary vision features. The primary advantage of the algorithms developed is their ability to learn to determine the visual quality of almost any fruit or vegetable with no need for specific modification and no a-priori knowledge. Moreover, since there is no assumption as to the type of blemish to be characterized, the algorithm is capable of distinguishing between stems and bruises. This enables sorting of fruit without knowing the fruits' orientation. A new algorithm for on-line clustering of data was developed. The algorithm's adaptability is designed to overcome some of the difficulties encountered when incrementally clustering sparse data and preserves information even with memory constraints. Large quantities of data (many images) of high dimensionality (due to multiple sensors) and new information arriving incrementally (a function of the temporal dynamics of any natural process) can now be processed. Furhermore, since the learning is done on-line, it can be implemented in real-time. The methodology developed was tested to determine external quality of tomatoes based on visual information. An improved model for color sorting which is stable and does not require recalibration for each season was developed for color determination. Excellent classification results were obtained for both color and firmness classification. Results indicted that maturity classification can be obtained using a drop-impact and a vision sensor in order to predict the storability and marketing of harvested fruits. In conclusion: We have been able to define quantitatively the critical parameters in the quality sorting and grading of both fresh market cantaloupes and tomatoes. We have been able to accomplish this using nondestructive measurements and in a manner consistent with expert human grading and in accordance with market acceptance. This research constructed and used large databases of both commodities, for comparative evaluation and optimization of expert system, statistical and/or neural network models. The models developed in this research were successfully tested, and should be applicable to a wide range of other fruits and vegetables. These findings are valuable for the development of on-line grading and sorting of agricultural produce through the incorporation of multiple measurement inputs that rapidly define quality in an automated manner, and in a manner consistent with the human graders and inspectors.
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