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You searched for subject:(reproducing kernel Hilbert space). Showing records 1 – 30 of 16747 total matches.

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Texas A&M University

1. Zhang, Nan. Adaptive Basis Sampling for Smoothing Splines.

Degree: PhD, Statistics, 2015, Texas A&M University

 Smoothing splines provide flexible nonparametric regression estimators. Penalized likelihood method is adopted when responses are from exponential families and multivariate models are constructed with certain… (more)

Subjects/Keywords: Nonparametric regression; Penalized likelihood; Reproducing kernel Hilbert space

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APA (6th Edition):

Zhang, N. (2015). Adaptive Basis Sampling for Smoothing Splines. (Doctoral Dissertation). Texas A&M University. Retrieved from http://hdl.handle.net/1969.1/155626

Chicago Manual of Style (16th Edition):

Zhang, Nan. “Adaptive Basis Sampling for Smoothing Splines.” 2015. Doctoral Dissertation, Texas A&M University. Accessed September 25, 2020. http://hdl.handle.net/1969.1/155626.

MLA Handbook (7th Edition):

Zhang, Nan. “Adaptive Basis Sampling for Smoothing Splines.” 2015. Web. 25 Sep 2020.

Vancouver:

Zhang N. Adaptive Basis Sampling for Smoothing Splines. [Internet] [Doctoral dissertation]. Texas A&M University; 2015. [cited 2020 Sep 25]. Available from: http://hdl.handle.net/1969.1/155626.

Council of Science Editors:

Zhang N. Adaptive Basis Sampling for Smoothing Splines. [Doctoral Dissertation]. Texas A&M University; 2015. Available from: http://hdl.handle.net/1969.1/155626

2. Crawford, Lorin Anthony. Bayesian Kernel Models for Statistical Genetics and Cancer Genomics .

Degree: 2017, Duke University

  The main contribution of this thesis is to examine the utility of kernel regression ap- proaches and variance component models for solving complex problems… (more)

Subjects/Keywords: Statistics; Biostatistics; Genetics; Epistasis; Radiogenomics; Reproducing kernel Hilbert space; Variance Component

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APA (6th Edition):

Crawford, L. A. (2017). Bayesian Kernel Models for Statistical Genetics and Cancer Genomics . (Thesis). Duke University. Retrieved from http://hdl.handle.net/10161/14539

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Crawford, Lorin Anthony. “Bayesian Kernel Models for Statistical Genetics and Cancer Genomics .” 2017. Thesis, Duke University. Accessed September 25, 2020. http://hdl.handle.net/10161/14539.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Crawford, Lorin Anthony. “Bayesian Kernel Models for Statistical Genetics and Cancer Genomics .” 2017. Web. 25 Sep 2020.

Vancouver:

Crawford LA. Bayesian Kernel Models for Statistical Genetics and Cancer Genomics . [Internet] [Thesis]. Duke University; 2017. [cited 2020 Sep 25]. Available from: http://hdl.handle.net/10161/14539.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Crawford LA. Bayesian Kernel Models for Statistical Genetics and Cancer Genomics . [Thesis]. Duke University; 2017. Available from: http://hdl.handle.net/10161/14539

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Penn State University

3. Zhan, Xiang. Kernel Machine Methods With Applications to High-throughout Data.

Degree: 2015, Penn State University

 Recently, handling large and high-dimensional data has become increasingly important with the rapid development of high-throughput technologies. This is also referred to as ``big data''… (more)

Subjects/Keywords: Kernel machines; Reproducing kernel Hilbert space; Genome-wide association study; Metabolite differential analysis

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APA (6th Edition):

Zhan, X. (2015). Kernel Machine Methods With Applications to High-throughout Data. (Thesis). Penn State University. Retrieved from https://submit-etda.libraries.psu.edu/catalog/24895

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Zhan, Xiang. “Kernel Machine Methods With Applications to High-throughout Data.” 2015. Thesis, Penn State University. Accessed September 25, 2020. https://submit-etda.libraries.psu.edu/catalog/24895.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Zhan, Xiang. “Kernel Machine Methods With Applications to High-throughout Data.” 2015. Web. 25 Sep 2020.

Vancouver:

Zhan X. Kernel Machine Methods With Applications to High-throughout Data. [Internet] [Thesis]. Penn State University; 2015. [cited 2020 Sep 25]. Available from: https://submit-etda.libraries.psu.edu/catalog/24895.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Zhan X. Kernel Machine Methods With Applications to High-throughout Data. [Thesis]. Penn State University; 2015. Available from: https://submit-etda.libraries.psu.edu/catalog/24895

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

4. Jordão, Thaís. Diferenciabilidade em espaços de Hilbert de reprodução sobre a esfera.

Degree: PhD, Matemática, 2012, University of São Paulo

Um espaço de Hilbert de reprodução (EHR) é um espaço de Hilbert de funções construído de maneira específica e única a partir de um núcleo… (more)

Subjects/Keywords: Diferenciabilidade; Differentiability; Esfera; Espaços de Hilbert de reprodução; Mercer Kernel; Núcleos de Mercer; Reproducing Kernel Hilbert space; Sphere

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APA (6th Edition):

Jordão, T. (2012). Diferenciabilidade em espaços de Hilbert de reprodução sobre a esfera. (Doctoral Dissertation). University of São Paulo. Retrieved from http://www.teses.usp.br/teses/disponiveis/55/55135/tde-29032012-103159/ ;

Chicago Manual of Style (16th Edition):

Jordão, Thaís. “Diferenciabilidade em espaços de Hilbert de reprodução sobre a esfera.” 2012. Doctoral Dissertation, University of São Paulo. Accessed September 25, 2020. http://www.teses.usp.br/teses/disponiveis/55/55135/tde-29032012-103159/ ;.

MLA Handbook (7th Edition):

Jordão, Thaís. “Diferenciabilidade em espaços de Hilbert de reprodução sobre a esfera.” 2012. Web. 25 Sep 2020.

Vancouver:

Jordão T. Diferenciabilidade em espaços de Hilbert de reprodução sobre a esfera. [Internet] [Doctoral dissertation]. University of São Paulo; 2012. [cited 2020 Sep 25]. Available from: http://www.teses.usp.br/teses/disponiveis/55/55135/tde-29032012-103159/ ;.

Council of Science Editors:

Jordão T. Diferenciabilidade em espaços de Hilbert de reprodução sobre a esfera. [Doctoral Dissertation]. University of São Paulo; 2012. Available from: http://www.teses.usp.br/teses/disponiveis/55/55135/tde-29032012-103159/ ;


Penn State University

5. Mirshani, Ardalan. Regularization Methods In Functional Data Analysis.

Degree: 2019, Penn State University

 New studies, surveys, and technologies are resulting in ever richer and more informative data sets. With the development of modern technology, Functional data analysis (FDA)… (more)

Subjects/Keywords: Functional Data; Differential Privacy; Hilbert Space; Variable Selection; Reproducing Kernel Hilbert Space; Oracle Property; Elastic Net; Smooth Estimate; Density Estimation

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APA (6th Edition):

Mirshani, A. (2019). Regularization Methods In Functional Data Analysis. (Thesis). Penn State University. Retrieved from https://submit-etda.libraries.psu.edu/catalog/16828azm245

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Mirshani, Ardalan. “Regularization Methods In Functional Data Analysis.” 2019. Thesis, Penn State University. Accessed September 25, 2020. https://submit-etda.libraries.psu.edu/catalog/16828azm245.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Mirshani, Ardalan. “Regularization Methods In Functional Data Analysis.” 2019. Web. 25 Sep 2020.

Vancouver:

Mirshani A. Regularization Methods In Functional Data Analysis. [Internet] [Thesis]. Penn State University; 2019. [cited 2020 Sep 25]. Available from: https://submit-etda.libraries.psu.edu/catalog/16828azm245.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Mirshani A. Regularization Methods In Functional Data Analysis. [Thesis]. Penn State University; 2019. Available from: https://submit-etda.libraries.psu.edu/catalog/16828azm245

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of Rochester

6. Xia, Changming; Liang, Hua. Generalized Semiparametric Linear Mixed-effects Models.

Degree: PhD, 2015, University of Rochester

 This dissertation proposes a novel method to compare tumor growth patterns and evaluates treatment effects using semiparametric linear mixed-effects (SLME) model, which allows a flexible… (more)

Subjects/Keywords: Mixed-effects; Generalized Linear Models; Semiparametric Regression; Generalized Maximum Likelihood; Reproducing Kernel Hilbert Space

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APA (6th Edition):

Xia, Changming; Liang, H. (2015). Generalized Semiparametric Linear Mixed-effects Models. (Doctoral Dissertation). University of Rochester. Retrieved from http://hdl.handle.net/1802/30126

Chicago Manual of Style (16th Edition):

Xia, Changming; Liang, Hua. “Generalized Semiparametric Linear Mixed-effects Models.” 2015. Doctoral Dissertation, University of Rochester. Accessed September 25, 2020. http://hdl.handle.net/1802/30126.

MLA Handbook (7th Edition):

Xia, Changming; Liang, Hua. “Generalized Semiparametric Linear Mixed-effects Models.” 2015. Web. 25 Sep 2020.

Vancouver:

Xia, Changming; Liang H. Generalized Semiparametric Linear Mixed-effects Models. [Internet] [Doctoral dissertation]. University of Rochester; 2015. [cited 2020 Sep 25]. Available from: http://hdl.handle.net/1802/30126.

Council of Science Editors:

Xia, Changming; Liang H. Generalized Semiparametric Linear Mixed-effects Models. [Doctoral Dissertation]. University of Rochester; 2015. Available from: http://hdl.handle.net/1802/30126


East Tennessee State University

7. Agrawal, Devanshu. The Complete Structure of Linear and Nonlinear Deformations of Frames on a Hilbert Space.

Degree: MS, Mathematical Sciences, 2016, East Tennessee State University

  A frame is a possibly linearly dependent set of vectors in a Hilbert space that facilitates the decomposition and reconstruction of vectors. A Parseval… (more)

Subjects/Keywords: Hilbert Space; Reproducing Kernel; Finite Frame; Gabor Frame; Fiber Bundle; Analysis; Mathematics

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APA (6th Edition):

Agrawal, D. (2016). The Complete Structure of Linear and Nonlinear Deformations of Frames on a Hilbert Space. (Masters Thesis). East Tennessee State University. Retrieved from https://dc.etsu.edu/etd/3003

Chicago Manual of Style (16th Edition):

Agrawal, Devanshu. “The Complete Structure of Linear and Nonlinear Deformations of Frames on a Hilbert Space.” 2016. Masters Thesis, East Tennessee State University. Accessed September 25, 2020. https://dc.etsu.edu/etd/3003.

MLA Handbook (7th Edition):

Agrawal, Devanshu. “The Complete Structure of Linear and Nonlinear Deformations of Frames on a Hilbert Space.” 2016. Web. 25 Sep 2020.

Vancouver:

Agrawal D. The Complete Structure of Linear and Nonlinear Deformations of Frames on a Hilbert Space. [Internet] [Masters thesis]. East Tennessee State University; 2016. [cited 2020 Sep 25]. Available from: https://dc.etsu.edu/etd/3003.

Council of Science Editors:

Agrawal D. The Complete Structure of Linear and Nonlinear Deformations of Frames on a Hilbert Space. [Masters Thesis]. East Tennessee State University; 2016. Available from: https://dc.etsu.edu/etd/3003


Iowa State University

8. Fortin, Daniel Clayton. Contributions to modeling spatially indexed functional data using a reproducing kernel Hilbert space framework.

Degree: 2015, Iowa State University

 In many instances, it is useful to view data as a collection of curves, particularly when the questions motivating the analysis relate to properties of… (more)

Subjects/Keywords: Statistics; functional data analysis; geostatistics; reproducing kernel Hilbert space; Statistics and Probability

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APA (6th Edition):

Fortin, D. C. (2015). Contributions to modeling spatially indexed functional data using a reproducing kernel Hilbert space framework. (Thesis). Iowa State University. Retrieved from https://lib.dr.iastate.edu/etd/14836

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Fortin, Daniel Clayton. “Contributions to modeling spatially indexed functional data using a reproducing kernel Hilbert space framework.” 2015. Thesis, Iowa State University. Accessed September 25, 2020. https://lib.dr.iastate.edu/etd/14836.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Fortin, Daniel Clayton. “Contributions to modeling spatially indexed functional data using a reproducing kernel Hilbert space framework.” 2015. Web. 25 Sep 2020.

Vancouver:

Fortin DC. Contributions to modeling spatially indexed functional data using a reproducing kernel Hilbert space framework. [Internet] [Thesis]. Iowa State University; 2015. [cited 2020 Sep 25]. Available from: https://lib.dr.iastate.edu/etd/14836.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Fortin DC. Contributions to modeling spatially indexed functional data using a reproducing kernel Hilbert space framework. [Thesis]. Iowa State University; 2015. Available from: https://lib.dr.iastate.edu/etd/14836

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Rice University

9. Luo, Zhenwei. Sparsity and Smoothness and Their Application in Cryo-EM Single Particle Analysis.

Degree: PhD, Natural Sciences, 2020, Rice University

 We presented a new 3D refinement method for Cryo-EM single particle analysis which can improve the resolution of final electron density map in this thesis.… (more)

Subjects/Keywords: Cryo-EM; 3D reconstruction; ill-posed inverse problem; smoothness; sparsity; reproducing kernel Hilbert space; CUDA

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APA (6th Edition):

Luo, Z. (2020). Sparsity and Smoothness and Their Application in Cryo-EM Single Particle Analysis. (Doctoral Dissertation). Rice University. Retrieved from http://hdl.handle.net/1911/108423

Chicago Manual of Style (16th Edition):

Luo, Zhenwei. “Sparsity and Smoothness and Their Application in Cryo-EM Single Particle Analysis.” 2020. Doctoral Dissertation, Rice University. Accessed September 25, 2020. http://hdl.handle.net/1911/108423.

MLA Handbook (7th Edition):

Luo, Zhenwei. “Sparsity and Smoothness and Their Application in Cryo-EM Single Particle Analysis.” 2020. Web. 25 Sep 2020.

Vancouver:

Luo Z. Sparsity and Smoothness and Their Application in Cryo-EM Single Particle Analysis. [Internet] [Doctoral dissertation]. Rice University; 2020. [cited 2020 Sep 25]. Available from: http://hdl.handle.net/1911/108423.

Council of Science Editors:

Luo Z. Sparsity and Smoothness and Their Application in Cryo-EM Single Particle Analysis. [Doctoral Dissertation]. Rice University; 2020. Available from: http://hdl.handle.net/1911/108423


Texas A&M University

10. Ren, Haobo. Functional inverse regression and reproducing kernel Hilbert space.

Degree: PhD, Statistics, 2006, Texas A&M University

 The basic philosophy of Functional Data Analysis (FDA) is to think of the observed data functions as elements of a possibly infinite-dimensional function space. Most… (more)

Subjects/Keywords: Functional data analaysis; Inverse Regression; Reproducing Kernel Hilbert Space

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APA (6th Edition):

Ren, H. (2006). Functional inverse regression and reproducing kernel Hilbert space. (Doctoral Dissertation). Texas A&M University. Retrieved from http://hdl.handle.net/1969.1/4203

Chicago Manual of Style (16th Edition):

Ren, Haobo. “Functional inverse regression and reproducing kernel Hilbert space.” 2006. Doctoral Dissertation, Texas A&M University. Accessed September 25, 2020. http://hdl.handle.net/1969.1/4203.

MLA Handbook (7th Edition):

Ren, Haobo. “Functional inverse regression and reproducing kernel Hilbert space.” 2006. Web. 25 Sep 2020.

Vancouver:

Ren H. Functional inverse regression and reproducing kernel Hilbert space. [Internet] [Doctoral dissertation]. Texas A&M University; 2006. [cited 2020 Sep 25]. Available from: http://hdl.handle.net/1969.1/4203.

Council of Science Editors:

Ren H. Functional inverse regression and reproducing kernel Hilbert space. [Doctoral Dissertation]. Texas A&M University; 2006. Available from: http://hdl.handle.net/1969.1/4203


Purdue University

11. Qu, Simeng. Functional regression models in the frame work of reproducing kernel Hilbert space.

Degree: PhD, Statistics, 2016, Purdue University

  The aim of this thesis is to systematically investigate some functional regression models for accurately quantifying the effect of functional predictors. In particular, three… (more)

Subjects/Keywords: Pure sciences; Dictionary learning; Functional Cox model; Functional linear regression; Functional regression; Reproducing kernel Hilbert space; Statistics and Probability

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APA (6th Edition):

Qu, S. (2016). Functional regression models in the frame work of reproducing kernel Hilbert space. (Doctoral Dissertation). Purdue University. Retrieved from https://docs.lib.purdue.edu/open_access_dissertations/991

Chicago Manual of Style (16th Edition):

Qu, Simeng. “Functional regression models in the frame work of reproducing kernel Hilbert space.” 2016. Doctoral Dissertation, Purdue University. Accessed September 25, 2020. https://docs.lib.purdue.edu/open_access_dissertations/991.

MLA Handbook (7th Edition):

Qu, Simeng. “Functional regression models in the frame work of reproducing kernel Hilbert space.” 2016. Web. 25 Sep 2020.

Vancouver:

Qu S. Functional regression models in the frame work of reproducing kernel Hilbert space. [Internet] [Doctoral dissertation]. Purdue University; 2016. [cited 2020 Sep 25]. Available from: https://docs.lib.purdue.edu/open_access_dissertations/991.

Council of Science Editors:

Qu S. Functional regression models in the frame work of reproducing kernel Hilbert space. [Doctoral Dissertation]. Purdue University; 2016. Available from: https://docs.lib.purdue.edu/open_access_dissertations/991

12. Sabree, Aqeeb A. Positive definite kernels, harmonic analysis, and boundary spaces: Drury-Arveson theory, and related.

Degree: PhD, Mathematics, 2019, University of Iowa

  A reproducing kernel Hilbert space (RKHS) is a Hilbert space ℋ of functions with the property that the values f(x) for f ∈ ℋ… (more)

Subjects/Keywords: Drury Arveson; Fock Space; Hilbert Function Space; Positive Definite Function; Reproducing Kernel; Reproducing Kernel Hilbert Space; Mathematics

…ABSTRACT A reproducing kernel Hilbert space (RKHS) is a Hilbert space H of… …reproducing kernel Hilbert space has an associated generalized boundary probability space. The… …Hilbert space. This reproducing kernel Hilbert space stems from boundary analysis of the Arveson… …which is also a reproducing kernel Hilbert space. The polynomial functions that are studied in… …positive definite kernel or reproducing kernel that lives in the reproducing kernel Hilbert space… 

Page 1 Page 2 Page 3 Page 4 Page 5 Page 6 Page 7

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APA (6th Edition):

Sabree, A. A. (2019). Positive definite kernels, harmonic analysis, and boundary spaces: Drury-Arveson theory, and related. (Doctoral Dissertation). University of Iowa. Retrieved from https://ir.uiowa.edu/etd/7023

Chicago Manual of Style (16th Edition):

Sabree, Aqeeb A. “Positive definite kernels, harmonic analysis, and boundary spaces: Drury-Arveson theory, and related.” 2019. Doctoral Dissertation, University of Iowa. Accessed September 25, 2020. https://ir.uiowa.edu/etd/7023.

MLA Handbook (7th Edition):

Sabree, Aqeeb A. “Positive definite kernels, harmonic analysis, and boundary spaces: Drury-Arveson theory, and related.” 2019. Web. 25 Sep 2020.

Vancouver:

Sabree AA. Positive definite kernels, harmonic analysis, and boundary spaces: Drury-Arveson theory, and related. [Internet] [Doctoral dissertation]. University of Iowa; 2019. [cited 2020 Sep 25]. Available from: https://ir.uiowa.edu/etd/7023.

Council of Science Editors:

Sabree AA. Positive definite kernels, harmonic analysis, and boundary spaces: Drury-Arveson theory, and related. [Doctoral Dissertation]. University of Iowa; 2019. Available from: https://ir.uiowa.edu/etd/7023


University of Kentucky

13. Ke, Chenlu. A NEW INDEPENDENCE MEASURE AND ITS APPLICATIONS IN HIGH DIMENSIONAL DATA ANALYSIS.

Degree: 2019, University of Kentucky

 This dissertation has three consecutive topics. First, we propose a novel class of independence measures for testing independence between two random vectors based on the… (more)

Subjects/Keywords: High dimensional data analysis; Independence; Reproducing Kernel Hilbert Space; Sufficient Dimension Reduction; Sufficient Variable Selection; Categorical Data Analysis; Multivariate Analysis; Statistics and Probability

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APA (6th Edition):

Ke, C. (2019). A NEW INDEPENDENCE MEASURE AND ITS APPLICATIONS IN HIGH DIMENSIONAL DATA ANALYSIS. (Doctoral Dissertation). University of Kentucky. Retrieved from https://uknowledge.uky.edu/statistics_etds/41

Chicago Manual of Style (16th Edition):

Ke, Chenlu. “A NEW INDEPENDENCE MEASURE AND ITS APPLICATIONS IN HIGH DIMENSIONAL DATA ANALYSIS.” 2019. Doctoral Dissertation, University of Kentucky. Accessed September 25, 2020. https://uknowledge.uky.edu/statistics_etds/41.

MLA Handbook (7th Edition):

Ke, Chenlu. “A NEW INDEPENDENCE MEASURE AND ITS APPLICATIONS IN HIGH DIMENSIONAL DATA ANALYSIS.” 2019. Web. 25 Sep 2020.

Vancouver:

Ke C. A NEW INDEPENDENCE MEASURE AND ITS APPLICATIONS IN HIGH DIMENSIONAL DATA ANALYSIS. [Internet] [Doctoral dissertation]. University of Kentucky; 2019. [cited 2020 Sep 25]. Available from: https://uknowledge.uky.edu/statistics_etds/41.

Council of Science Editors:

Ke C. A NEW INDEPENDENCE MEASURE AND ITS APPLICATIONS IN HIGH DIMENSIONAL DATA ANALYSIS. [Doctoral Dissertation]. University of Kentucky; 2019. Available from: https://uknowledge.uky.edu/statistics_etds/41

14. Bui, Thi Thien Trang. Modèle de régression pour des données non-Euclidiennes en grande dimension. Application à la classification de taxons en anatomie computationnelle. : Regression model for high-dimensional non-euclidean data. Application to the classification of taxa in the computational anatomy.

Degree: Docteur es, Mathématiques et Applications, 2019, Toulouse, INSA

Dans cette thèse, nous étudions un modèle de régression avec des entrées de type distribution et le problème de test d'hypothèse pour la détection de… (more)

Subjects/Keywords: Régression; Reproduction de l'espace de Hilbert du noyau; Distance de Wasserstein; Émission otoacoustique évoquée transitoire; Taux de séparation; Tests adaptatifs; Méthodes du noyau; Test agrégé; Regression; Reproducing kernel Hilbert space; Wasserstein distance; Transient evoked otoacoustic emission; Separation rates; Adaptive tests; Kernel methods; Aggregated test; 511

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APA (6th Edition):

Bui, T. T. T. (2019). Modèle de régression pour des données non-Euclidiennes en grande dimension. Application à la classification de taxons en anatomie computationnelle. : Regression model for high-dimensional non-euclidean data. Application to the classification of taxa in the computational anatomy. (Doctoral Dissertation). Toulouse, INSA. Retrieved from http://www.theses.fr/2019ISAT0021

Chicago Manual of Style (16th Edition):

Bui, Thi Thien Trang. “Modèle de régression pour des données non-Euclidiennes en grande dimension. Application à la classification de taxons en anatomie computationnelle. : Regression model for high-dimensional non-euclidean data. Application to the classification of taxa in the computational anatomy.” 2019. Doctoral Dissertation, Toulouse, INSA. Accessed September 25, 2020. http://www.theses.fr/2019ISAT0021.

MLA Handbook (7th Edition):

Bui, Thi Thien Trang. “Modèle de régression pour des données non-Euclidiennes en grande dimension. Application à la classification de taxons en anatomie computationnelle. : Regression model for high-dimensional non-euclidean data. Application to the classification of taxa in the computational anatomy.” 2019. Web. 25 Sep 2020.

Vancouver:

Bui TTT. Modèle de régression pour des données non-Euclidiennes en grande dimension. Application à la classification de taxons en anatomie computationnelle. : Regression model for high-dimensional non-euclidean data. Application to the classification of taxa in the computational anatomy. [Internet] [Doctoral dissertation]. Toulouse, INSA; 2019. [cited 2020 Sep 25]. Available from: http://www.theses.fr/2019ISAT0021.

Council of Science Editors:

Bui TTT. Modèle de régression pour des données non-Euclidiennes en grande dimension. Application à la classification de taxons en anatomie computationnelle. : Regression model for high-dimensional non-euclidean data. Application to the classification of taxa in the computational anatomy. [Doctoral Dissertation]. Toulouse, INSA; 2019. Available from: http://www.theses.fr/2019ISAT0021

15. Sangeetha, R. A Framework for Admissible Kernel Function in Support Vector Machines using Lévy Distribution;.

Degree: Computer Science, 2015, Avinashilingam Deemed University For Women

With the massive amount of data being generated in everyday life it is increasingly important to develop a powerful framework for analysis interpretation and extraction… (more)

Subjects/Keywords: Support vector machine; kernel function; Hilbert space

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APA (6th Edition):

Sangeetha, R. (2015). A Framework for Admissible Kernel Function in Support Vector Machines using Lévy Distribution;. (Thesis). Avinashilingam Deemed University For Women. Retrieved from http://shodhganga.inflibnet.ac.in/handle/10603/36872

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Sangeetha, R. “A Framework for Admissible Kernel Function in Support Vector Machines using Lévy Distribution;.” 2015. Thesis, Avinashilingam Deemed University For Women. Accessed September 25, 2020. http://shodhganga.inflibnet.ac.in/handle/10603/36872.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Sangeetha, R. “A Framework for Admissible Kernel Function in Support Vector Machines using Lévy Distribution;.” 2015. Web. 25 Sep 2020.

Vancouver:

Sangeetha R. A Framework for Admissible Kernel Function in Support Vector Machines using Lévy Distribution;. [Internet] [Thesis]. Avinashilingam Deemed University For Women; 2015. [cited 2020 Sep 25]. Available from: http://shodhganga.inflibnet.ac.in/handle/10603/36872.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Sangeetha R. A Framework for Admissible Kernel Function in Support Vector Machines using Lévy Distribution;. [Thesis]. Avinashilingam Deemed University For Women; 2015. Available from: http://shodhganga.inflibnet.ac.in/handle/10603/36872

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Georgia Tech

16. Kingravi, Hassan. Reduced-set models for improving the training and execution speed of kernel methods.

Degree: PhD, Electrical and Computer Engineering, 2014, Georgia Tech

 This thesis aims to contribute to the area of kernel methods, which are a class of machine learning methods known for their wide applicability and… (more)

Subjects/Keywords: Machine learning; Kernel methods; Reproducing kernel Hilbert spaces; Adaptive control; Manifold learning; Algorithms; Computer algorithms; Kernel functions; Support vector machines

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Kingravi, H. (2014). Reduced-set models for improving the training and execution speed of kernel methods. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/51799

Chicago Manual of Style (16th Edition):

Kingravi, Hassan. “Reduced-set models for improving the training and execution speed of kernel methods.” 2014. Doctoral Dissertation, Georgia Tech. Accessed September 25, 2020. http://hdl.handle.net/1853/51799.

MLA Handbook (7th Edition):

Kingravi, Hassan. “Reduced-set models for improving the training and execution speed of kernel methods.” 2014. Web. 25 Sep 2020.

Vancouver:

Kingravi H. Reduced-set models for improving the training and execution speed of kernel methods. [Internet] [Doctoral dissertation]. Georgia Tech; 2014. [cited 2020 Sep 25]. Available from: http://hdl.handle.net/1853/51799.

Council of Science Editors:

Kingravi H. Reduced-set models for improving the training and execution speed of kernel methods. [Doctoral Dissertation]. Georgia Tech; 2014. Available from: http://hdl.handle.net/1853/51799


Penn State University

17. Solea, Eftychia. Nonparametric graphical models for functional data.

Degree: 2017, Penn State University

 This thesis studies the development of graphical models for functional data; that is, graphical models whose observations on the vertices are realizations of random functions.… (more)

Subjects/Keywords: Additive conditional independence; additive correlation operator; additive precision operator; EEG; fMRI; gaussian graphical model; reproducing kernel Hilbert space.; Functional Principal Component Analysis; Hilbert space- valued random elements; Karhunen-Loeve expansion; Rank Correlation; Rank Transformation.

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APA (6th Edition):

Solea, E. (2017). Nonparametric graphical models for functional data. (Thesis). Penn State University. Retrieved from https://submit-etda.libraries.psu.edu/catalog/13763exs392

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Solea, Eftychia. “Nonparametric graphical models for functional data.” 2017. Thesis, Penn State University. Accessed September 25, 2020. https://submit-etda.libraries.psu.edu/catalog/13763exs392.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Solea, Eftychia. “Nonparametric graphical models for functional data.” 2017. Web. 25 Sep 2020.

Vancouver:

Solea E. Nonparametric graphical models for functional data. [Internet] [Thesis]. Penn State University; 2017. [cited 2020 Sep 25]. Available from: https://submit-etda.libraries.psu.edu/catalog/13763exs392.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Solea E. Nonparametric graphical models for functional data. [Thesis]. Penn State University; 2017. Available from: https://submit-etda.libraries.psu.edu/catalog/13763exs392

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Université Montpellier II

18. Henchiri, Yousri. L'approche Support Vector Machines (SVM) pour le traitement des données fonctionnelles : Support Vector Machines (SVM) for Fonctional Data Analysis.

Degree: Docteur es, Biostatistique, 2013, Université Montpellier II

L'Analyse des Données Fonctionnelles est un domaine important et dynamique en statistique. Elle offre des outils efficaces et propose de nouveaux développements méthodologiques et théoriques… (more)

Subjects/Keywords: Analyse des Données Fonctionnelles; Support Vector Machines; Quantiles de régression; Apprentissage statistique; Apprentissage supervisé; Espace de Hilbert à noyau reproduisant; Functional Data Analysis; Support Vector Machines; Quantile Regression; Statistical learning; Supervised learning; Reproducing kernel Hilbert space

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Henchiri, Y. (2013). L'approche Support Vector Machines (SVM) pour le traitement des données fonctionnelles : Support Vector Machines (SVM) for Fonctional Data Analysis. (Doctoral Dissertation). Université Montpellier II. Retrieved from http://www.theses.fr/2013MON20187

Chicago Manual of Style (16th Edition):

Henchiri, Yousri. “L'approche Support Vector Machines (SVM) pour le traitement des données fonctionnelles : Support Vector Machines (SVM) for Fonctional Data Analysis.” 2013. Doctoral Dissertation, Université Montpellier II. Accessed September 25, 2020. http://www.theses.fr/2013MON20187.

MLA Handbook (7th Edition):

Henchiri, Yousri. “L'approche Support Vector Machines (SVM) pour le traitement des données fonctionnelles : Support Vector Machines (SVM) for Fonctional Data Analysis.” 2013. Web. 25 Sep 2020.

Vancouver:

Henchiri Y. L'approche Support Vector Machines (SVM) pour le traitement des données fonctionnelles : Support Vector Machines (SVM) for Fonctional Data Analysis. [Internet] [Doctoral dissertation]. Université Montpellier II; 2013. [cited 2020 Sep 25]. Available from: http://www.theses.fr/2013MON20187.

Council of Science Editors:

Henchiri Y. L'approche Support Vector Machines (SVM) pour le traitement des données fonctionnelles : Support Vector Machines (SVM) for Fonctional Data Analysis. [Doctoral Dissertation]. Université Montpellier II; 2013. Available from: http://www.theses.fr/2013MON20187


University of Waterloo

19. Martin, Robert. Bandlimited functions, curved manifolds, and self-adjoint extensions of symmetric operators.

Degree: 2008, University of Waterloo

 Sampling theory is an active field of research that spans a variety of disciplines from communication engineering to pure mathematics. Sampling theory provides the crucial… (more)

Subjects/Keywords: applied harmonic analysis; self-adjoint extensions of symmetric operators; Paley-Wiener space; reproducing kernel Hilbert space; manifolds; differential operators

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APA (6th Edition):

Martin, R. (2008). Bandlimited functions, curved manifolds, and self-adjoint extensions of symmetric operators. (Thesis). University of Waterloo. Retrieved from http://hdl.handle.net/10012/3698

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Martin, Robert. “Bandlimited functions, curved manifolds, and self-adjoint extensions of symmetric operators.” 2008. Thesis, University of Waterloo. Accessed September 25, 2020. http://hdl.handle.net/10012/3698.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Martin, Robert. “Bandlimited functions, curved manifolds, and self-adjoint extensions of symmetric operators.” 2008. Web. 25 Sep 2020.

Vancouver:

Martin R. Bandlimited functions, curved manifolds, and self-adjoint extensions of symmetric operators. [Internet] [Thesis]. University of Waterloo; 2008. [cited 2020 Sep 25]. Available from: http://hdl.handle.net/10012/3698.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Martin R. Bandlimited functions, curved manifolds, and self-adjoint extensions of symmetric operators. [Thesis]. University of Waterloo; 2008. Available from: http://hdl.handle.net/10012/3698

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Texas A&M University

20. Shin, Hyejin. Infinite dimensional discrimination and classification.

Degree: PhD, Statistics, 2007, Texas A&M University

 Modern data collection methods are now frequently returning observations that should be viewed as the result of digitized recording or sampling from stochastic processes rather… (more)

Subjects/Keywords: Fisher's linear discriminant analysis; Canonical correlation analysis; Stochastic porcesses; Reproducing kernel Hilbert space; Functional data

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Shin, H. (2007). Infinite dimensional discrimination and classification. (Doctoral Dissertation). Texas A&M University. Retrieved from http://hdl.handle.net/1969.1/5832

Chicago Manual of Style (16th Edition):

Shin, Hyejin. “Infinite dimensional discrimination and classification.” 2007. Doctoral Dissertation, Texas A&M University. Accessed September 25, 2020. http://hdl.handle.net/1969.1/5832.

MLA Handbook (7th Edition):

Shin, Hyejin. “Infinite dimensional discrimination and classification.” 2007. Web. 25 Sep 2020.

Vancouver:

Shin H. Infinite dimensional discrimination and classification. [Internet] [Doctoral dissertation]. Texas A&M University; 2007. [cited 2020 Sep 25]. Available from: http://hdl.handle.net/1969.1/5832.

Council of Science Editors:

Shin H. Infinite dimensional discrimination and classification. [Doctoral Dissertation]. Texas A&M University; 2007. Available from: http://hdl.handle.net/1969.1/5832

21. Pan, Yue. Currents- and varifolds-based registration of lung vessels and lung surfaces.

Degree: MS, Electrical and Computer Engineering, 2016, University of Iowa

  This thesis compares and contrasts currents- and varifolds-based diffeomorphic image registration approaches for registering tree-like structures in the lung and surface of the lung.… (more)

Subjects/Keywords: currents; Diffeomorphic Image Registration; Reproducing Kernel Hilbert Space (RKHS); varifolds; Electrical and Computer Engineering

…possible vector fields 4 W , where W is a Reproducing Kernel Hilbert Space (RKHS). In… …possible vector fields W , where W is a Reproducing Kernel Hilbert Space (RKHS)… …connection between a Hilbert space and its (continuous) dual space. If the underlying… …3.1.2 Sensitivity to the Number/Position of Momenta and the Kernel Size of the RKHS… …deformation kernel. . . . . . 27 3.2 SACD Registration error… 

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Pan, Y. (2016). Currents- and varifolds-based registration of lung vessels and lung surfaces. (Masters Thesis). University of Iowa. Retrieved from https://ir.uiowa.edu/etd/2257

Chicago Manual of Style (16th Edition):

Pan, Yue. “Currents- and varifolds-based registration of lung vessels and lung surfaces.” 2016. Masters Thesis, University of Iowa. Accessed September 25, 2020. https://ir.uiowa.edu/etd/2257.

MLA Handbook (7th Edition):

Pan, Yue. “Currents- and varifolds-based registration of lung vessels and lung surfaces.” 2016. Web. 25 Sep 2020.

Vancouver:

Pan Y. Currents- and varifolds-based registration of lung vessels and lung surfaces. [Internet] [Masters thesis]. University of Iowa; 2016. [cited 2020 Sep 25]. Available from: https://ir.uiowa.edu/etd/2257.

Council of Science Editors:

Pan Y. Currents- and varifolds-based registration of lung vessels and lung surfaces. [Masters Thesis]. University of Iowa; 2016. Available from: https://ir.uiowa.edu/etd/2257

22. Lessig, Christian. Modern Foundations of Light Transport Simulation.

Degree: 2012, University of Toronto

Light transport simulation aims at the numerical computation of the propagation of visible electromagnetic energy in macroscopic environments. In this thesis, we develop the foundations… (more)

Subjects/Keywords: light transport simulation; geometric mechanics; reproducing kernel Hilbert space; 0756; 0756; 0984

…4 Reproducing Kernel Bases for Light Transport Simulations 4.1 Reproducing Kernel Bases… …in applications. Our answer will be Hilbert space expansions whose coefficients are… …function values as expansion coefficients. Moreover, for such reproducing kernel bases close to… …idea, and using reproducing kernel bases together with Galerkin projection enables us to… …mapping, within a Hilbert space setting. This provides insight into their working principles and… 

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Lessig, C. (2012). Modern Foundations of Light Transport Simulation. (Doctoral Dissertation). University of Toronto. Retrieved from http://hdl.handle.net/1807/32808

Chicago Manual of Style (16th Edition):

Lessig, Christian. “Modern Foundations of Light Transport Simulation.” 2012. Doctoral Dissertation, University of Toronto. Accessed September 25, 2020. http://hdl.handle.net/1807/32808.

MLA Handbook (7th Edition):

Lessig, Christian. “Modern Foundations of Light Transport Simulation.” 2012. Web. 25 Sep 2020.

Vancouver:

Lessig C. Modern Foundations of Light Transport Simulation. [Internet] [Doctoral dissertation]. University of Toronto; 2012. [cited 2020 Sep 25]. Available from: http://hdl.handle.net/1807/32808.

Council of Science Editors:

Lessig C. Modern Foundations of Light Transport Simulation. [Doctoral Dissertation]. University of Toronto; 2012. Available from: http://hdl.handle.net/1807/32808


University of Georgia

23. Sun, Xiaoxiao. Nonparametric methods for big and complex datasets under a reproducing kernel Hilbert space framework.

Degree: 2018, University of Georgia

 Large and complex data have been generated routinely from various sources, for instance, time course biological studies and social media. Classic nonparametric models, such as… (more)

Subjects/Keywords: smoothing spline ANOVA; smoothing parameters selection; optimal smoothing parameters; function-on-function regression; representer theorem; penalized least squares; reproducing kernel Hilbert space; minimax convergence rate; time course RNA-seq; differentially expressed genes

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APA (6th Edition):

Sun, X. (2018). Nonparametric methods for big and complex datasets under a reproducing kernel Hilbert space framework. (Thesis). University of Georgia. Retrieved from http://hdl.handle.net/10724/38552

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Sun, Xiaoxiao. “Nonparametric methods for big and complex datasets under a reproducing kernel Hilbert space framework.” 2018. Thesis, University of Georgia. Accessed September 25, 2020. http://hdl.handle.net/10724/38552.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Sun, Xiaoxiao. “Nonparametric methods for big and complex datasets under a reproducing kernel Hilbert space framework.” 2018. Web. 25 Sep 2020.

Vancouver:

Sun X. Nonparametric methods for big and complex datasets under a reproducing kernel Hilbert space framework. [Internet] [Thesis]. University of Georgia; 2018. [cited 2020 Sep 25]. Available from: http://hdl.handle.net/10724/38552.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Sun X. Nonparametric methods for big and complex datasets under a reproducing kernel Hilbert space framework. [Thesis]. University of Georgia; 2018. Available from: http://hdl.handle.net/10724/38552

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Penn State University

24. Taoufik, Bahaeddine. FUNCTIONAL DATA BASED INFERENCE FOR HIGH FREQUENCY FINANCIAL DATA.

Degree: 2016, Penn State University

 This thesis is concerned with developing new functional data techniques for high frequency financial applications. Chapter 1 of the thesis introduces Functional Data Analysis (FDA)… (more)

Subjects/Keywords: Functional Data; Nonlinear Functional Regression; Cross-section of returns; Cumulative intraday returns; Reproducing kernel Hilbert spaces

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APA (6th Edition):

Taoufik, B. (2016). FUNCTIONAL DATA BASED INFERENCE FOR HIGH FREQUENCY FINANCIAL DATA. (Thesis). Penn State University. Retrieved from https://submit-etda.libraries.psu.edu/catalog/13392but129

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Taoufik, Bahaeddine. “FUNCTIONAL DATA BASED INFERENCE FOR HIGH FREQUENCY FINANCIAL DATA.” 2016. Thesis, Penn State University. Accessed September 25, 2020. https://submit-etda.libraries.psu.edu/catalog/13392but129.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Taoufik, Bahaeddine. “FUNCTIONAL DATA BASED INFERENCE FOR HIGH FREQUENCY FINANCIAL DATA.” 2016. Web. 25 Sep 2020.

Vancouver:

Taoufik B. FUNCTIONAL DATA BASED INFERENCE FOR HIGH FREQUENCY FINANCIAL DATA. [Internet] [Thesis]. Penn State University; 2016. [cited 2020 Sep 25]. Available from: https://submit-etda.libraries.psu.edu/catalog/13392but129.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Taoufik B. FUNCTIONAL DATA BASED INFERENCE FOR HIGH FREQUENCY FINANCIAL DATA. [Thesis]. Penn State University; 2016. Available from: https://submit-etda.libraries.psu.edu/catalog/13392but129

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Iowa State University

25. Dixit, Anand Ulhas. Developments in MCMC diagnostics and sparse Bayesian learning models.

Degree: 2018, Iowa State University

 This dissertation consists of three research articles on the topic of Markov chain Monte Carlo (MCMC) diagnostics and sparse Bayesian learning models. The first article… (more)

Subjects/Keywords: Geometric ergodicity; Kullback Leibler divergence; Monte Carlo standard error; Posterior impropriety; Relevance vector machine; Reproducing kernel Hilbert spaces; Statistics and Probability

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APA (6th Edition):

Dixit, A. U. (2018). Developments in MCMC diagnostics and sparse Bayesian learning models. (Thesis). Iowa State University. Retrieved from https://lib.dr.iastate.edu/etd/17175

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Dixit, Anand Ulhas. “Developments in MCMC diagnostics and sparse Bayesian learning models.” 2018. Thesis, Iowa State University. Accessed September 25, 2020. https://lib.dr.iastate.edu/etd/17175.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Dixit, Anand Ulhas. “Developments in MCMC diagnostics and sparse Bayesian learning models.” 2018. Web. 25 Sep 2020.

Vancouver:

Dixit AU. Developments in MCMC diagnostics and sparse Bayesian learning models. [Internet] [Thesis]. Iowa State University; 2018. [cited 2020 Sep 25]. Available from: https://lib.dr.iastate.edu/etd/17175.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Dixit AU. Developments in MCMC diagnostics and sparse Bayesian learning models. [Thesis]. Iowa State University; 2018. Available from: https://lib.dr.iastate.edu/etd/17175

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


ETH Zürich

26. Butt, Jemil. An RKHS approach to modelling and inference for spatiotemporal geodetic data with applications to terrestrial radar interferometry.

Degree: 2019, ETH Zürich

Reproducing kernel Hilbert spaces (RKHS) are interpretable as normed spaces of functions furnished with a probability distribution and as such are especially suitable for modelling… (more)

Subjects/Keywords: Reproducing kernel Hilbert spaces, Geostatistics, Radar interferometry, Adjustment; info:eu-repo/classification/ddc/550; info:eu-repo/classification/ddc/510; Earth sciences; Mathematics

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APA (6th Edition):

Butt, J. (2019). An RKHS approach to modelling and inference for spatiotemporal geodetic data with applications to terrestrial radar interferometry. (Doctoral Dissertation). ETH Zürich. Retrieved from http://hdl.handle.net/20.500.11850/346619

Chicago Manual of Style (16th Edition):

Butt, Jemil. “An RKHS approach to modelling and inference for spatiotemporal geodetic data with applications to terrestrial radar interferometry.” 2019. Doctoral Dissertation, ETH Zürich. Accessed September 25, 2020. http://hdl.handle.net/20.500.11850/346619.

MLA Handbook (7th Edition):

Butt, Jemil. “An RKHS approach to modelling and inference for spatiotemporal geodetic data with applications to terrestrial radar interferometry.” 2019. Web. 25 Sep 2020.

Vancouver:

Butt J. An RKHS approach to modelling and inference for spatiotemporal geodetic data with applications to terrestrial radar interferometry. [Internet] [Doctoral dissertation]. ETH Zürich; 2019. [cited 2020 Sep 25]. Available from: http://hdl.handle.net/20.500.11850/346619.

Council of Science Editors:

Butt J. An RKHS approach to modelling and inference for spatiotemporal geodetic data with applications to terrestrial radar interferometry. [Doctoral Dissertation]. ETH Zürich; 2019. Available from: http://hdl.handle.net/20.500.11850/346619

27. Ammanouil, Rita. Contributions au démélange non-supervisé et non-linéaire de données hyperspectrales : Contributions to unsupervised and nonlinear unmixing of hyperspectral data.

Degree: Docteur es, Sciences de l'ingénieur, 2016, Université Côte d'Azur (ComUE)

Le démélange spectral est l’un des problèmes centraux pour l’exploitation des images hyperspectrales. En raison de la faible résolution spatiale des imageurs hyperspectraux en télédetection,… (more)

Subjects/Keywords: Données hyperspectrales; Démélange non-supervisé; Démélange non-linéaire; Algorithme des directions altérnées (ADMM); Régularisation de type groupe lasso; Régularisation avec le Laplacian; Espace de Hilbert à noyau reproduisant (RKHS); Hyperspectral data; Unsupervised unmixing; Nonlinear unmixing; Alternating direction method of multipliers (ADMM); Group lasso regularization; Laplacian regularization; Vector valued reproducing kernel Hilbert space (RKHS)

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APA (6th Edition):

Ammanouil, R. (2016). Contributions au démélange non-supervisé et non-linéaire de données hyperspectrales : Contributions to unsupervised and nonlinear unmixing of hyperspectral data. (Doctoral Dissertation). Université Côte d'Azur (ComUE). Retrieved from http://www.theses.fr/2016AZUR4079

Chicago Manual of Style (16th Edition):

Ammanouil, Rita. “Contributions au démélange non-supervisé et non-linéaire de données hyperspectrales : Contributions to unsupervised and nonlinear unmixing of hyperspectral data.” 2016. Doctoral Dissertation, Université Côte d'Azur (ComUE). Accessed September 25, 2020. http://www.theses.fr/2016AZUR4079.

MLA Handbook (7th Edition):

Ammanouil, Rita. “Contributions au démélange non-supervisé et non-linéaire de données hyperspectrales : Contributions to unsupervised and nonlinear unmixing of hyperspectral data.” 2016. Web. 25 Sep 2020.

Vancouver:

Ammanouil R. Contributions au démélange non-supervisé et non-linéaire de données hyperspectrales : Contributions to unsupervised and nonlinear unmixing of hyperspectral data. [Internet] [Doctoral dissertation]. Université Côte d'Azur (ComUE); 2016. [cited 2020 Sep 25]. Available from: http://www.theses.fr/2016AZUR4079.

Council of Science Editors:

Ammanouil R. Contributions au démélange non-supervisé et non-linéaire de données hyperspectrales : Contributions to unsupervised and nonlinear unmixing of hyperspectral data. [Doctoral Dissertation]. Université Côte d'Azur (ComUE); 2016. Available from: http://www.theses.fr/2016AZUR4079


Georgia Tech

28. Mumford, Michael Leslie. Applications of reproducing kernels in Hilbert spaces.

Degree: MS, Applied Mathematics, 1972, Georgia Tech

Subjects/Keywords: Kernel functions; Hilbert space

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APA (6th Edition):

Mumford, M. L. (1972). Applications of reproducing kernels in Hilbert spaces. (Masters Thesis). Georgia Tech. Retrieved from http://hdl.handle.net/1853/28742

Chicago Manual of Style (16th Edition):

Mumford, Michael Leslie. “Applications of reproducing kernels in Hilbert spaces.” 1972. Masters Thesis, Georgia Tech. Accessed September 25, 2020. http://hdl.handle.net/1853/28742.

MLA Handbook (7th Edition):

Mumford, Michael Leslie. “Applications of reproducing kernels in Hilbert spaces.” 1972. Web. 25 Sep 2020.

Vancouver:

Mumford ML. Applications of reproducing kernels in Hilbert spaces. [Internet] [Masters thesis]. Georgia Tech; 1972. [cited 2020 Sep 25]. Available from: http://hdl.handle.net/1853/28742.

Council of Science Editors:

Mumford ML. Applications of reproducing kernels in Hilbert spaces. [Masters Thesis]. Georgia Tech; 1972. Available from: http://hdl.handle.net/1853/28742

29. Ferreira, José Claudinei. Operadores integrais positivos e espaços de Hilbert de reprodução.

Degree: PhD, Matemática, 2010, University of São Paulo

Este trabalho é dedicado ao estudo de propriedades teóricas dos operadores integrais positivos em \'L POT. 2(́X; u), quando X é um espaço topológico localmente… (more)

Subjects/Keywords: Decaimento de autovalores; Decay rates of eigenvalues; Espaços de Hilbert de reprodução; Mercer theorem; Núcleos positivos definidos; Positive definite kernels; Reproducing kernel Hilbert spaces; Teorema de Mercer

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Ferreira, J. C. (2010). Operadores integrais positivos e espaços de Hilbert de reprodução. (Doctoral Dissertation). University of São Paulo. Retrieved from http://www.teses.usp.br/teses/disponiveis/55/55135/tde-17082010-100716/ ;

Chicago Manual of Style (16th Edition):

Ferreira, José Claudinei. “Operadores integrais positivos e espaços de Hilbert de reprodução.” 2010. Doctoral Dissertation, University of São Paulo. Accessed September 25, 2020. http://www.teses.usp.br/teses/disponiveis/55/55135/tde-17082010-100716/ ;.

MLA Handbook (7th Edition):

Ferreira, José Claudinei. “Operadores integrais positivos e espaços de Hilbert de reprodução.” 2010. Web. 25 Sep 2020.

Vancouver:

Ferreira JC. Operadores integrais positivos e espaços de Hilbert de reprodução. [Internet] [Doctoral dissertation]. University of São Paulo; 2010. [cited 2020 Sep 25]. Available from: http://www.teses.usp.br/teses/disponiveis/55/55135/tde-17082010-100716/ ;.

Council of Science Editors:

Ferreira JC. Operadores integrais positivos e espaços de Hilbert de reprodução. [Doctoral Dissertation]. University of São Paulo; 2010. Available from: http://www.teses.usp.br/teses/disponiveis/55/55135/tde-17082010-100716/ ;

30. Barbosa, Victor Simões. Universalidade e ortogonalidade em espaços de Hilbert de reprodução.

Degree: Mestrado, Matemática, 2013, University of São Paulo

Neste trabalho analisamos o papel das funções layout de um núcleo positivo definido K sobre um espaço topológico de Hausdor E com relação a duas… (more)

Subjects/Keywords: Espaços de Hilbert de reprodução; Feature maps; Função layout; Núcleos positivos definidos; Orthogonality; Ortogonalidade; Positive definite Kernels; Reproducing Kernel Hilbert spaces; Universabilidade; Universality

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Barbosa, V. S. (2013). Universalidade e ortogonalidade em espaços de Hilbert de reprodução. (Masters Thesis). University of São Paulo. Retrieved from http://www.teses.usp.br/teses/disponiveis/55/55135/tde-18032013-142251/ ;

Chicago Manual of Style (16th Edition):

Barbosa, Victor Simões. “Universalidade e ortogonalidade em espaços de Hilbert de reprodução.” 2013. Masters Thesis, University of São Paulo. Accessed September 25, 2020. http://www.teses.usp.br/teses/disponiveis/55/55135/tde-18032013-142251/ ;.

MLA Handbook (7th Edition):

Barbosa, Victor Simões. “Universalidade e ortogonalidade em espaços de Hilbert de reprodução.” 2013. Web. 25 Sep 2020.

Vancouver:

Barbosa VS. Universalidade e ortogonalidade em espaços de Hilbert de reprodução. [Internet] [Masters thesis]. University of São Paulo; 2013. [cited 2020 Sep 25]. Available from: http://www.teses.usp.br/teses/disponiveis/55/55135/tde-18032013-142251/ ;.

Council of Science Editors:

Barbosa VS. Universalidade e ortogonalidade em espaços de Hilbert de reprodução. [Masters Thesis]. University of São Paulo; 2013. Available from: http://www.teses.usp.br/teses/disponiveis/55/55135/tde-18032013-142251/ ;

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