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You searched for subject:(Kernel Methods). Showing records 1 – 30 of 144 total matches.

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1. Kanagawa, Motonobu. A Monte Carlo Method for Hilbert Space Embeddings of Distributions and Its Application to Filtering in State Space Models : モンテカルロ法による確率分布のヒルベルト空間埋め込みとその状態空間モデルのフィルタリングへの応用; モンテカルロホウ ニ ヨル カクリツ ブンプ ノ ヒルベルト クウカン ウメコミ ト ソノ ジョウタイ クウカン モデル ノ フィルタリング エノ オウヨウ.

Degree: Nara Institute of Science and Technology / 奈良先端科学技術大学院大学

Subjects/Keywords: kernel methods

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

APA (6th Edition):

Kanagawa, M. (n.d.). A Monte Carlo Method for Hilbert Space Embeddings of Distributions and Its Application to Filtering in State Space Models : モンテカルロ法による確率分布のヒルベルト空間埋め込みとその状態空間モデルのフィルタリングへの応用; モンテカルロホウ ニ ヨル カクリツ ブンプ ノ ヒルベルト クウカン ウメコミ ト ソノ ジョウタイ クウカン モデル ノ フィルタリング エノ オウヨウ. (Thesis). Nara Institute of Science and Technology / 奈良先端科学技術大学院大学. Retrieved from http://hdl.handle.net/10061/8703

Note: this citation may be lacking information needed for this citation format:
No year of publication.
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Kanagawa, Motonobu. “A Monte Carlo Method for Hilbert Space Embeddings of Distributions and Its Application to Filtering in State Space Models : モンテカルロ法による確率分布のヒルベルト空間埋め込みとその状態空間モデルのフィルタリングへの応用; モンテカルロホウ ニ ヨル カクリツ ブンプ ノ ヒルベルト クウカン ウメコミ ト ソノ ジョウタイ クウカン モデル ノ フィルタリング エノ オウヨウ.” Thesis, Nara Institute of Science and Technology / 奈良先端科学技術大学院大学. Accessed October 31, 2020. http://hdl.handle.net/10061/8703.

Note: this citation may be lacking information needed for this citation format:
No year of publication.
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Kanagawa, Motonobu. “A Monte Carlo Method for Hilbert Space Embeddings of Distributions and Its Application to Filtering in State Space Models : モンテカルロ法による確率分布のヒルベルト空間埋め込みとその状態空間モデルのフィルタリングへの応用; モンテカルロホウ ニ ヨル カクリツ ブンプ ノ ヒルベルト クウカン ウメコミ ト ソノ ジョウタイ クウカン モデル ノ フィルタリング エノ オウヨウ.” Web. 31 Oct 2020.

Note: this citation may be lacking information needed for this citation format:
No year of publication.

Vancouver:

Kanagawa M. A Monte Carlo Method for Hilbert Space Embeddings of Distributions and Its Application to Filtering in State Space Models : モンテカルロ法による確率分布のヒルベルト空間埋め込みとその状態空間モデルのフィルタリングへの応用; モンテカルロホウ ニ ヨル カクリツ ブンプ ノ ヒルベルト クウカン ウメコミ ト ソノ ジョウタイ クウカン モデル ノ フィルタリング エノ オウヨウ. [Internet] [Thesis]. Nara Institute of Science and Technology / 奈良先端科学技術大学院大学; [cited 2020 Oct 31]. Available from: http://hdl.handle.net/10061/8703.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
No year of publication.

Council of Science Editors:

Kanagawa M. A Monte Carlo Method for Hilbert Space Embeddings of Distributions and Its Application to Filtering in State Space Models : モンテカルロ法による確率分布のヒルベルト空間埋め込みとその状態空間モデルのフィルタリングへの応用; モンテカルロホウ ニ ヨル カクリツ ブンプ ノ ヒルベルト クウカン ウメコミ ト ソノ ジョウタイ クウカン モデル ノ フィルタリング エノ オウヨウ. [Thesis]. Nara Institute of Science and Technology / 奈良先端科学技術大学院大学; Available from: http://hdl.handle.net/10061/8703

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
No year of publication.

2. Sun, Fangzheng. Kernel Coherence Encoders.

Degree: MS, 2018, Worcester Polytechnic Institute

 In this thesis, we introduce a novel model based on the idea of autoencoders. Different from a classic autoencoder which reconstructs its own inputs through… (more)

Subjects/Keywords: CCA; Kernel methods; Autoencoders; KCCA

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APA (6th Edition):

Sun, F. (2018). Kernel Coherence Encoders. (Thesis). Worcester Polytechnic Institute. Retrieved from etd-042318-222257 ; https://digitalcommons.wpi.edu/etd-theses/252

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, Fangzheng. “Kernel Coherence Encoders.” 2018. Thesis, Worcester Polytechnic Institute. Accessed October 31, 2020. etd-042318-222257 ; https://digitalcommons.wpi.edu/etd-theses/252.

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

MLA Handbook (7th Edition):

Sun, Fangzheng. “Kernel Coherence Encoders.” 2018. Web. 31 Oct 2020.

Vancouver:

Sun F. Kernel Coherence Encoders. [Internet] [Thesis]. Worcester Polytechnic Institute; 2018. [cited 2020 Oct 31]. Available from: etd-042318-222257 ; https://digitalcommons.wpi.edu/etd-theses/252.

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

Council of Science Editors:

Sun F. Kernel Coherence Encoders. [Thesis]. Worcester Polytechnic Institute; 2018. Available from: etd-042318-222257 ; https://digitalcommons.wpi.edu/etd-theses/252

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


University of Texas – Austin

3. Si, Si, Ph.D. Large-scale non-linear prediction with applications.

Degree: PhD, Computer science, 2016, University of Texas – Austin

 With an immense growth in data, there is a great need for training and testing machine learning models on very large data sets. Several standard… (more)

Subjects/Keywords: Kernel methods; Classification; Decision trees

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

APA (6th Edition):

Si, Si, P. D. (2016). Large-scale non-linear prediction with applications. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/43583

Chicago Manual of Style (16th Edition):

Si, Si, Ph D. “Large-scale non-linear prediction with applications.” 2016. Doctoral Dissertation, University of Texas – Austin. Accessed October 31, 2020. http://hdl.handle.net/2152/43583.

MLA Handbook (7th Edition):

Si, Si, Ph D. “Large-scale non-linear prediction with applications.” 2016. Web. 31 Oct 2020.

Vancouver:

Si, Si PD. Large-scale non-linear prediction with applications. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2016. [cited 2020 Oct 31]. Available from: http://hdl.handle.net/2152/43583.

Council of Science Editors:

Si, Si PD. Large-scale non-linear prediction with applications. [Doctoral Dissertation]. University of Texas – Austin; 2016. Available from: http://hdl.handle.net/2152/43583


Penn State University

4. Lin, Junli. Copula Versions of RKHS-Based and Distance-Based Criteria.

Degree: 2017, Penn State University

 Four general classes of statistics in hypothesis testing and corresponding measures are those based on reproducing kernels or distances. Among the most popular criteria for… (more)

Subjects/Keywords: kernel methods; permutation method; two-sample problems; copula; kernel methods; permutation methods; V-statistic

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

APA (6th Edition):

Lin, J. (2017). Copula Versions of RKHS-Based and Distance-Based Criteria. (Thesis). Penn State University. Retrieved from https://submit-etda.libraries.psu.edu/catalog/14485jul268

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):

Lin, Junli. “Copula Versions of RKHS-Based and Distance-Based Criteria.” 2017. Thesis, Penn State University. Accessed October 31, 2020. https://submit-etda.libraries.psu.edu/catalog/14485jul268.

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

MLA Handbook (7th Edition):

Lin, Junli. “Copula Versions of RKHS-Based and Distance-Based Criteria.” 2017. Web. 31 Oct 2020.

Vancouver:

Lin J. Copula Versions of RKHS-Based and Distance-Based Criteria. [Internet] [Thesis]. Penn State University; 2017. [cited 2020 Oct 31]. Available from: https://submit-etda.libraries.psu.edu/catalog/14485jul268.

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

Council of Science Editors:

Lin J. Copula Versions of RKHS-Based and Distance-Based Criteria. [Thesis]. Penn State University; 2017. Available from: https://submit-etda.libraries.psu.edu/catalog/14485jul268

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

5. Ben Khedhiri, Issam. Kernel methods for advanced statistical process control.

Degree: 2011, Technische Universität Dortmund

 This thesis investigated development and application of Kernel methods to enhance Statistical Process Control procedures. The first part of this thesis discussed the development of… (more)

Subjects/Keywords: Kernel methods; Statistical process controll; 310

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

APA (6th Edition):

Ben Khedhiri, I. (2011). Kernel methods for advanced statistical process control. (Thesis). Technische Universität Dortmund. Retrieved from http://hdl.handle.net/2003/29282

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):

Ben Khedhiri, Issam. “Kernel methods for advanced statistical process control.” 2011. Thesis, Technische Universität Dortmund. Accessed October 31, 2020. http://hdl.handle.net/2003/29282.

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

MLA Handbook (7th Edition):

Ben Khedhiri, Issam. “Kernel methods for advanced statistical process control.” 2011. Web. 31 Oct 2020.

Vancouver:

Ben Khedhiri I. Kernel methods for advanced statistical process control. [Internet] [Thesis]. Technische Universität Dortmund; 2011. [cited 2020 Oct 31]. Available from: http://hdl.handle.net/2003/29282.

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

Council of Science Editors:

Ben Khedhiri I. Kernel methods for advanced statistical process control. [Thesis]. Technische Universität Dortmund; 2011. Available from: http://hdl.handle.net/2003/29282

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

6. Ben Khedhiri, Issam. Kernel methods for advanced statistical process control.

Degree: 2012, Technische Universität Dortmund

 This thesis investigated development and application of Kernel methods to enhance Statistical Process Control procedures. The first part of this thesis discussed the development of… (more)

Subjects/Keywords: Kernel methods; Statistical process controll; 310

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

APA (6th Edition):

Ben Khedhiri, I. (2012). Kernel methods for advanced statistical process control. (Doctoral Dissertation). Technische Universität Dortmund. Retrieved from http://dx.doi.org/10.17877/DE290R-3854

Chicago Manual of Style (16th Edition):

Ben Khedhiri, Issam. “Kernel methods for advanced statistical process control.” 2012. Doctoral Dissertation, Technische Universität Dortmund. Accessed October 31, 2020. http://dx.doi.org/10.17877/DE290R-3854.

MLA Handbook (7th Edition):

Ben Khedhiri, Issam. “Kernel methods for advanced statistical process control.” 2012. Web. 31 Oct 2020.

Vancouver:

Ben Khedhiri I. Kernel methods for advanced statistical process control. [Internet] [Doctoral dissertation]. Technische Universität Dortmund; 2012. [cited 2020 Oct 31]. Available from: http://dx.doi.org/10.17877/DE290R-3854.

Council of Science Editors:

Ben Khedhiri I. Kernel methods for advanced statistical process control. [Doctoral Dissertation]. Technische Universität Dortmund; 2012. Available from: http://dx.doi.org/10.17877/DE290R-3854


Rice University

7. Patel, Raajen. oASIS: Adaptive Column Sampling for Kernel Matrix Approximation.

Degree: MS, Engineering, 2015, Rice University

Kernel or similarity matrices are essential for many state-of-the-art approaches to classification, clustering, and dimensionality reduction. For large datasets, the cost of forming and factoring… (more)

Subjects/Keywords: Machine Learning; Matrix Approximation; Kernel Methods

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APA (6th Edition):

Patel, R. (2015). oASIS: Adaptive Column Sampling for Kernel Matrix Approximation. (Masters Thesis). Rice University. Retrieved from http://hdl.handle.net/1911/88435

Chicago Manual of Style (16th Edition):

Patel, Raajen. “oASIS: Adaptive Column Sampling for Kernel Matrix Approximation.” 2015. Masters Thesis, Rice University. Accessed October 31, 2020. http://hdl.handle.net/1911/88435.

MLA Handbook (7th Edition):

Patel, Raajen. “oASIS: Adaptive Column Sampling for Kernel Matrix Approximation.” 2015. Web. 31 Oct 2020.

Vancouver:

Patel R. oASIS: Adaptive Column Sampling for Kernel Matrix Approximation. [Internet] [Masters thesis]. Rice University; 2015. [cited 2020 Oct 31]. Available from: http://hdl.handle.net/1911/88435.

Council of Science Editors:

Patel R. oASIS: Adaptive Column Sampling for Kernel Matrix Approximation. [Masters Thesis]. Rice University; 2015. Available from: http://hdl.handle.net/1911/88435


University of Waterloo

8. Elbagoury, Ahmed. Exemplar-based Kernel Preserving Embedding.

Degree: 2016, University of Waterloo

 With the rapid increase of available data, it becomes computationally harder to extract useful information, specially in the case of high-dimensional data. Choosing a representative… (more)

Subjects/Keywords: Exemplar; Embedding; Kernel Methods; Topic Detection

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APA (6th Edition):

Elbagoury, A. (2016). Exemplar-based Kernel Preserving Embedding. (Thesis). University of Waterloo. Retrieved from http://hdl.handle.net/10012/10435

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):

Elbagoury, Ahmed. “Exemplar-based Kernel Preserving Embedding.” 2016. Thesis, University of Waterloo. Accessed October 31, 2020. http://hdl.handle.net/10012/10435.

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

MLA Handbook (7th Edition):

Elbagoury, Ahmed. “Exemplar-based Kernel Preserving Embedding.” 2016. Web. 31 Oct 2020.

Vancouver:

Elbagoury A. Exemplar-based Kernel Preserving Embedding. [Internet] [Thesis]. University of Waterloo; 2016. [cited 2020 Oct 31]. Available from: http://hdl.handle.net/10012/10435.

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

Council of Science Editors:

Elbagoury A. Exemplar-based Kernel Preserving Embedding. [Thesis]. University of Waterloo; 2016. Available from: http://hdl.handle.net/10012/10435

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


University of Cambridge

9. Rowland, Mark. Structure in machine learning : graphical models and Monte Carlo methods.

Degree: PhD, 2018, University of Cambridge

 This thesis is concerned with two main areas: approximate inference in discrete graphical models, and random embeddings for dimensionality reduction and approximate inference in kernel(more)

Subjects/Keywords: Mathematics; Statistics; Machine Learning; Graphical Models; Monte Carlo Methods; Kernel Methods

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APA (6th Edition):

Rowland, M. (2018). Structure in machine learning : graphical models and Monte Carlo methods. (Doctoral Dissertation). University of Cambridge. Retrieved from https://doi.org/10.17863/CAM.34784 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.763924

Chicago Manual of Style (16th Edition):

Rowland, Mark. “Structure in machine learning : graphical models and Monte Carlo methods.” 2018. Doctoral Dissertation, University of Cambridge. Accessed October 31, 2020. https://doi.org/10.17863/CAM.34784 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.763924.

MLA Handbook (7th Edition):

Rowland, Mark. “Structure in machine learning : graphical models and Monte Carlo methods.” 2018. Web. 31 Oct 2020.

Vancouver:

Rowland M. Structure in machine learning : graphical models and Monte Carlo methods. [Internet] [Doctoral dissertation]. University of Cambridge; 2018. [cited 2020 Oct 31]. Available from: https://doi.org/10.17863/CAM.34784 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.763924.

Council of Science Editors:

Rowland M. Structure in machine learning : graphical models and Monte Carlo methods. [Doctoral Dissertation]. University of Cambridge; 2018. Available from: https://doi.org/10.17863/CAM.34784 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.763924


Kennesaw State University

10. Le, Linh. Deep Embedding Kernel.

Degree: PhD, Statistics and Analytical Sciences, 2019, Kennesaw State University

Kernel methods and deep learning are two major branches of machine learning that have achieved numerous successes in both analytics and artificial intelligence. While having… (more)

Subjects/Keywords: Deep Embedding Kernel; Deep Kernel; Deep Learning; Machine Learning; Kernel Methods; Supervised Learning; Computer Sciences; Statistics and Probability

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APA (6th Edition):

Le, L. (2019). Deep Embedding Kernel. (Doctoral Dissertation). Kennesaw State University. Retrieved from https://digitalcommons.kennesaw.edu/dataphd_etd/1

Chicago Manual of Style (16th Edition):

Le, Linh. “Deep Embedding Kernel.” 2019. Doctoral Dissertation, Kennesaw State University. Accessed October 31, 2020. https://digitalcommons.kennesaw.edu/dataphd_etd/1.

MLA Handbook (7th Edition):

Le, Linh. “Deep Embedding Kernel.” 2019. Web. 31 Oct 2020.

Vancouver:

Le L. Deep Embedding Kernel. [Internet] [Doctoral dissertation]. Kennesaw State University; 2019. [cited 2020 Oct 31]. Available from: https://digitalcommons.kennesaw.edu/dataphd_etd/1.

Council of Science Editors:

Le L. Deep Embedding Kernel. [Doctoral Dissertation]. Kennesaw State University; 2019. Available from: https://digitalcommons.kennesaw.edu/dataphd_etd/1


Georgia Tech

11. 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 October 31, 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. 31 Oct 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 Oct 31]. 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


University of Pretoria

12. [No author]. Nonlinear fault detection and diagnosis using Kernel based techniques applied to a pilot distillation colomn .

Degree: 2008, University of Pretoria

 Fault detection and diagnosis is an important problem in process engineering. In this dissertation, use of multivariate techniques for fault detection and diagnosis is explored… (more)

Subjects/Keywords: Statistical process control; Fault diagnosis; Fault detection; Kernel based methods; UCTD

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APA (6th Edition):

author], [. (2008). Nonlinear fault detection and diagnosis using Kernel based techniques applied to a pilot distillation colomn . (Masters Thesis). University of Pretoria. Retrieved from http://upetd.up.ac.za/thesis/available/etd-01152008-125258/

Chicago Manual of Style (16th Edition):

author], [No. “Nonlinear fault detection and diagnosis using Kernel based techniques applied to a pilot distillation colomn .” 2008. Masters Thesis, University of Pretoria. Accessed October 31, 2020. http://upetd.up.ac.za/thesis/available/etd-01152008-125258/.

MLA Handbook (7th Edition):

author], [No. “Nonlinear fault detection and diagnosis using Kernel based techniques applied to a pilot distillation colomn .” 2008. Web. 31 Oct 2020.

Vancouver:

author] [. Nonlinear fault detection and diagnosis using Kernel based techniques applied to a pilot distillation colomn . [Internet] [Masters thesis]. University of Pretoria; 2008. [cited 2020 Oct 31]. Available from: http://upetd.up.ac.za/thesis/available/etd-01152008-125258/.

Council of Science Editors:

author] [. Nonlinear fault detection and diagnosis using Kernel based techniques applied to a pilot distillation colomn . [Masters Thesis]. University of Pretoria; 2008. Available from: http://upetd.up.ac.za/thesis/available/etd-01152008-125258/


Mississippi State University

13. Ganapathiraju, Aravind. Support Vector Machines for Speech Recognition.

Degree: PhD, Electrical and Computer Engineering, 2002, Mississippi State University

 Hidden Markov models (HMM) with Gaussian mixture observation densities are the dominant approach in speech recognition. These systems typically use a representational model for acoustic… (more)

Subjects/Keywords: classification; kernel methods; acoustic modeling

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APA (6th Edition):

Ganapathiraju, A. (2002). Support Vector Machines for Speech Recognition. (Doctoral Dissertation). Mississippi State University. Retrieved from http://sun.library.msstate.edu/ETD-db/theses/available/etd-02202002-111027/ ;

Chicago Manual of Style (16th Edition):

Ganapathiraju, Aravind. “Support Vector Machines for Speech Recognition.” 2002. Doctoral Dissertation, Mississippi State University. Accessed October 31, 2020. http://sun.library.msstate.edu/ETD-db/theses/available/etd-02202002-111027/ ;.

MLA Handbook (7th Edition):

Ganapathiraju, Aravind. “Support Vector Machines for Speech Recognition.” 2002. Web. 31 Oct 2020.

Vancouver:

Ganapathiraju A. Support Vector Machines for Speech Recognition. [Internet] [Doctoral dissertation]. Mississippi State University; 2002. [cited 2020 Oct 31]. Available from: http://sun.library.msstate.edu/ETD-db/theses/available/etd-02202002-111027/ ;.

Council of Science Editors:

Ganapathiraju A. Support Vector Machines for Speech Recognition. [Doctoral Dissertation]. Mississippi State University; 2002. Available from: http://sun.library.msstate.edu/ETD-db/theses/available/etd-02202002-111027/ ;

14. Sarma, T Hitendra. Some techniques to speed-up k-means and kernel k-means clustering methods for large datasets.

Degree: Computer Science, 2013, Jawaharlal Nehru Technological University, Anantapuram

Data clustering is an unsupervised learning activity which is a process of finding natural groups (clusters) present in the given dataset (i.e., the given set… (more)

Subjects/Keywords: Kernel k-means clustering methods; Speed-Up K-Means

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APA (6th Edition):

Sarma, T. H. (2013). Some techniques to speed-up k-means and kernel k-means clustering methods for large datasets. (Thesis). Jawaharlal Nehru Technological University, Anantapuram. Retrieved from http://shodhganga.inflibnet.ac.in/handle/10603/13964

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):

Sarma, T Hitendra. “Some techniques to speed-up k-means and kernel k-means clustering methods for large datasets.” 2013. Thesis, Jawaharlal Nehru Technological University, Anantapuram. Accessed October 31, 2020. http://shodhganga.inflibnet.ac.in/handle/10603/13964.

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

MLA Handbook (7th Edition):

Sarma, T Hitendra. “Some techniques to speed-up k-means and kernel k-means clustering methods for large datasets.” 2013. Web. 31 Oct 2020.

Vancouver:

Sarma TH. Some techniques to speed-up k-means and kernel k-means clustering methods for large datasets. [Internet] [Thesis]. Jawaharlal Nehru Technological University, Anantapuram; 2013. [cited 2020 Oct 31]. Available from: http://shodhganga.inflibnet.ac.in/handle/10603/13964.

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

Council of Science Editors:

Sarma TH. Some techniques to speed-up k-means and kernel k-means clustering methods for large datasets. [Thesis]. Jawaharlal Nehru Technological University, Anantapuram; 2013. Available from: http://shodhganga.inflibnet.ac.in/handle/10603/13964

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


Texas A&M University

15. Katiyar, Ashish. Determination of Cancer Tissue Heterogeneity.

Degree: MS, Electrical Engineering, 2017, Texas A&M University

 Understanding the heterogeneous nature of cancer tissue is a very important problem in cancer research. It can give insights into the cause of disease, its… (more)

Subjects/Keywords: Heterogeneity; Bayesian methods; Metropolis Hastings; Kernel Density Estimation

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APA (6th Edition):

Katiyar, A. (2017). Determination of Cancer Tissue Heterogeneity. (Masters Thesis). Texas A&M University. Retrieved from http://hdl.handle.net/1969.1/161403

Chicago Manual of Style (16th Edition):

Katiyar, Ashish. “Determination of Cancer Tissue Heterogeneity.” 2017. Masters Thesis, Texas A&M University. Accessed October 31, 2020. http://hdl.handle.net/1969.1/161403.

MLA Handbook (7th Edition):

Katiyar, Ashish. “Determination of Cancer Tissue Heterogeneity.” 2017. Web. 31 Oct 2020.

Vancouver:

Katiyar A. Determination of Cancer Tissue Heterogeneity. [Internet] [Masters thesis]. Texas A&M University; 2017. [cited 2020 Oct 31]. Available from: http://hdl.handle.net/1969.1/161403.

Council of Science Editors:

Katiyar A. Determination of Cancer Tissue Heterogeneity. [Masters Thesis]. Texas A&M University; 2017. Available from: http://hdl.handle.net/1969.1/161403


Penn State University

16. Hua, Wen-yu. Kernel Methods for Neuroimaging Genomewide Association Studies.

Degree: 2014, Penn State University

 Measuring high-dimensional dependence is a difficult and important problem in the fields of statistics and machine learning, and is often being used for applications in… (more)

Subjects/Keywords: Kernel methods; GWAS; Neuroimaging analysis; Multiple comparison procedures.

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

APA (6th Edition):

Hua, W. (2014). Kernel Methods for Neuroimaging Genomewide Association Studies. (Thesis). Penn State University. Retrieved from https://submit-etda.libraries.psu.edu/catalog/22418

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):

Hua, Wen-yu. “Kernel Methods for Neuroimaging Genomewide Association Studies.” 2014. Thesis, Penn State University. Accessed October 31, 2020. https://submit-etda.libraries.psu.edu/catalog/22418.

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

MLA Handbook (7th Edition):

Hua, Wen-yu. “Kernel Methods for Neuroimaging Genomewide Association Studies.” 2014. Web. 31 Oct 2020.

Vancouver:

Hua W. Kernel Methods for Neuroimaging Genomewide Association Studies. [Internet] [Thesis]. Penn State University; 2014. [cited 2020 Oct 31]. Available from: https://submit-etda.libraries.psu.edu/catalog/22418.

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

Council of Science Editors:

Hua W. Kernel Methods for Neuroimaging Genomewide Association Studies. [Thesis]. Penn State University; 2014. Available from: https://submit-etda.libraries.psu.edu/catalog/22418

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


Penn State University

17. Straub, Benjamin Marshall. A STUDY OF APPROXIMATIONS AND DATA REDUCTION TECHNIQUES FOR KERNEL REGULARIZED LEAST SQUARES.

Degree: 2018, Penn State University

 Researchers using machine-learning algorithms are encountering problems of data storage and computation time with the advent of Big Data in almost all aspects of life… (more)

Subjects/Keywords: Kernel Methods; Machine-Learning; Ridge; Regularization; Random Projections; Clustering; Least-Squares

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APA (6th Edition):

Straub, B. M. (2018). A STUDY OF APPROXIMATIONS AND DATA REDUCTION TECHNIQUES FOR KERNEL REGULARIZED LEAST SQUARES. (Thesis). Penn State University. Retrieved from https://submit-etda.libraries.psu.edu/catalog/15177bbs5179

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):

Straub, Benjamin Marshall. “A STUDY OF APPROXIMATIONS AND DATA REDUCTION TECHNIQUES FOR KERNEL REGULARIZED LEAST SQUARES.” 2018. Thesis, Penn State University. Accessed October 31, 2020. https://submit-etda.libraries.psu.edu/catalog/15177bbs5179.

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

MLA Handbook (7th Edition):

Straub, Benjamin Marshall. “A STUDY OF APPROXIMATIONS AND DATA REDUCTION TECHNIQUES FOR KERNEL REGULARIZED LEAST SQUARES.” 2018. Web. 31 Oct 2020.

Vancouver:

Straub BM. A STUDY OF APPROXIMATIONS AND DATA REDUCTION TECHNIQUES FOR KERNEL REGULARIZED LEAST SQUARES. [Internet] [Thesis]. Penn State University; 2018. [cited 2020 Oct 31]. Available from: https://submit-etda.libraries.psu.edu/catalog/15177bbs5179.

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

Council of Science Editors:

Straub BM. A STUDY OF APPROXIMATIONS AND DATA REDUCTION TECHNIQUES FOR KERNEL REGULARIZED LEAST SQUARES. [Thesis]. Penn State University; 2018. Available from: https://submit-etda.libraries.psu.edu/catalog/15177bbs5179

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


Penn State University

18. Rao, Aniruddha Rajendra. COMPARISON OF DIFFERENT DENSITY ESTIMATORS FOR INFINITE DIMENSIONAL EXPONENTIAL FAMILIES.

Degree: 2019, Penn State University

 In this thesis, we consider the problem of estimating an unknown density, p_o belonging to an infinite dimensional exponential family P parametrized by functions in… (more)

Subjects/Keywords: Kernel Methods; Density Estimation; Exponential Family; Computation; Infinite Dimension

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

APA (6th Edition):

Rao, A. R. (2019). COMPARISON OF DIFFERENT DENSITY ESTIMATORS FOR INFINITE DIMENSIONAL EXPONENTIAL FAMILIES. (Thesis). Penn State University. Retrieved from https://submit-etda.libraries.psu.edu/catalog/16246arr30

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):

Rao, Aniruddha Rajendra. “COMPARISON OF DIFFERENT DENSITY ESTIMATORS FOR INFINITE DIMENSIONAL EXPONENTIAL FAMILIES.” 2019. Thesis, Penn State University. Accessed October 31, 2020. https://submit-etda.libraries.psu.edu/catalog/16246arr30.

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

MLA Handbook (7th Edition):

Rao, Aniruddha Rajendra. “COMPARISON OF DIFFERENT DENSITY ESTIMATORS FOR INFINITE DIMENSIONAL EXPONENTIAL FAMILIES.” 2019. Web. 31 Oct 2020.

Vancouver:

Rao AR. COMPARISON OF DIFFERENT DENSITY ESTIMATORS FOR INFINITE DIMENSIONAL EXPONENTIAL FAMILIES. [Internet] [Thesis]. Penn State University; 2019. [cited 2020 Oct 31]. Available from: https://submit-etda.libraries.psu.edu/catalog/16246arr30.

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

Council of Science Editors:

Rao AR. COMPARISON OF DIFFERENT DENSITY ESTIMATORS FOR INFINITE DIMENSIONAL EXPONENTIAL FAMILIES. [Thesis]. Penn State University; 2019. Available from: https://submit-etda.libraries.psu.edu/catalog/16246arr30

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


University of Pretoria

19. Phillpotts, David Nicholas Charles. Nonlinear fault detection and diagnosis using Kernel based techniques applied to a pilot distillation colomn.

Degree: MEng, Chemical Engineering, 2008, University of Pretoria

 Fault detection and diagnosis is an important problem in process engineering. In this dissertation, use of multivariate techniques for fault detection and diagnosis is explored… (more)

Subjects/Keywords: Statistical process control; Fault diagnosis; Fault detection; Kernel based methods; UCTD

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

APA (6th Edition):

Phillpotts, D. N. (2008). Nonlinear fault detection and diagnosis using Kernel based techniques applied to a pilot distillation colomn. (Masters Thesis). University of Pretoria. Retrieved from http://hdl.handle.net/2263/23256

Chicago Manual of Style (16th Edition):

Phillpotts, David Nicholas. “Nonlinear fault detection and diagnosis using Kernel based techniques applied to a pilot distillation colomn.” 2008. Masters Thesis, University of Pretoria. Accessed October 31, 2020. http://hdl.handle.net/2263/23256.

MLA Handbook (7th Edition):

Phillpotts, David Nicholas. “Nonlinear fault detection and diagnosis using Kernel based techniques applied to a pilot distillation colomn.” 2008. Web. 31 Oct 2020.

Vancouver:

Phillpotts DN. Nonlinear fault detection and diagnosis using Kernel based techniques applied to a pilot distillation colomn. [Internet] [Masters thesis]. University of Pretoria; 2008. [cited 2020 Oct 31]. Available from: http://hdl.handle.net/2263/23256.

Council of Science Editors:

Phillpotts DN. Nonlinear fault detection and diagnosis using Kernel based techniques applied to a pilot distillation colomn. [Masters Thesis]. University of Pretoria; 2008. Available from: http://hdl.handle.net/2263/23256


Tampere University

20. Chumachenko, Kateryna. Multi-view Subspace Learning for Large-Scale Multi-Modal Data Analysis .

Degree: 2019, Tampere University

 Dimensionality reduction methods play a big role within the modern machine learning techniques, and subspace learning is one of the common approaches to it. Although… (more)

Subjects/Keywords: machine learning; subspace learning; dimensionality reduction; kernel methods; discriminant analysis

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APA (6th Edition):

Chumachenko, K. (2019). Multi-view Subspace Learning for Large-Scale Multi-Modal Data Analysis . (Masters Thesis). Tampere University. Retrieved from https://trepo.tuni.fi//handle/10024/116541

Chicago Manual of Style (16th Edition):

Chumachenko, Kateryna. “Multi-view Subspace Learning for Large-Scale Multi-Modal Data Analysis .” 2019. Masters Thesis, Tampere University. Accessed October 31, 2020. https://trepo.tuni.fi//handle/10024/116541.

MLA Handbook (7th Edition):

Chumachenko, Kateryna. “Multi-view Subspace Learning for Large-Scale Multi-Modal Data Analysis .” 2019. Web. 31 Oct 2020.

Vancouver:

Chumachenko K. Multi-view Subspace Learning for Large-Scale Multi-Modal Data Analysis . [Internet] [Masters thesis]. Tampere University; 2019. [cited 2020 Oct 31]. Available from: https://trepo.tuni.fi//handle/10024/116541.

Council of Science Editors:

Chumachenko K. Multi-view Subspace Learning for Large-Scale Multi-Modal Data Analysis . [Masters Thesis]. Tampere University; 2019. Available from: https://trepo.tuni.fi//handle/10024/116541

21. Brouard, Céline. Inférence de réseaux d'interaction protéine-protéine par apprentissage statistique : Protein-protein interaction network inference using statistical learning.

Degree: Docteur es, Bioinformatique, 2013, Evry-Val d'Essonne

L'objectif de cette thèse est de développer des outils de prédiction d'interactions entre protéines qui puissent être appliqués en particulier sur le réseau d’interaction autour… (more)

Subjects/Keywords: Prédiction de liens; Link prediction; Kernel methods; Protein-protein interaction

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

APA (6th Edition):

Brouard, C. (2013). Inférence de réseaux d'interaction protéine-protéine par apprentissage statistique : Protein-protein interaction network inference using statistical learning. (Doctoral Dissertation). Evry-Val d'Essonne. Retrieved from http://www.theses.fr/2013EVRY0006

Chicago Manual of Style (16th Edition):

Brouard, Céline. “Inférence de réseaux d'interaction protéine-protéine par apprentissage statistique : Protein-protein interaction network inference using statistical learning.” 2013. Doctoral Dissertation, Evry-Val d'Essonne. Accessed October 31, 2020. http://www.theses.fr/2013EVRY0006.

MLA Handbook (7th Edition):

Brouard, Céline. “Inférence de réseaux d'interaction protéine-protéine par apprentissage statistique : Protein-protein interaction network inference using statistical learning.” 2013. Web. 31 Oct 2020.

Vancouver:

Brouard C. Inférence de réseaux d'interaction protéine-protéine par apprentissage statistique : Protein-protein interaction network inference using statistical learning. [Internet] [Doctoral dissertation]. Evry-Val d'Essonne; 2013. [cited 2020 Oct 31]. Available from: http://www.theses.fr/2013EVRY0006.

Council of Science Editors:

Brouard C. Inférence de réseaux d'interaction protéine-protéine par apprentissage statistique : Protein-protein interaction network inference using statistical learning. [Doctoral Dissertation]. Evry-Val d'Essonne; 2013. Available from: http://www.theses.fr/2013EVRY0006


University of Illinois – Chicago

22. Liu, Anqi. Robust Prediction Methods for Covariate Shift and Active Learning.

Degree: 2018, University of Illinois – Chicago

 In real world machine learning applications, it is often not very realistic to assume that the training data distribution aligns with the testing data distribution.… (more)

Subjects/Keywords: Covariate Shift; Active Learning; Robust Learning; Classification; Kernel Methods

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

APA (6th Edition):

Liu, A. (2018). Robust Prediction Methods for Covariate Shift and Active Learning. (Thesis). University of Illinois – Chicago. Retrieved from http://hdl.handle.net/10027/22988

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):

Liu, Anqi. “Robust Prediction Methods for Covariate Shift and Active Learning.” 2018. Thesis, University of Illinois – Chicago. Accessed October 31, 2020. http://hdl.handle.net/10027/22988.

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

MLA Handbook (7th Edition):

Liu, Anqi. “Robust Prediction Methods for Covariate Shift and Active Learning.” 2018. Web. 31 Oct 2020.

Vancouver:

Liu A. Robust Prediction Methods for Covariate Shift and Active Learning. [Internet] [Thesis]. University of Illinois – Chicago; 2018. [cited 2020 Oct 31]. Available from: http://hdl.handle.net/10027/22988.

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

Council of Science Editors:

Liu A. Robust Prediction Methods for Covariate Shift and Active Learning. [Thesis]. University of Illinois – Chicago; 2018. Available from: http://hdl.handle.net/10027/22988

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


Université Catholique de Louvain

23. Paul, Jérôme. Feature selection from heterogeneous biomedical data.

Degree: 2015, Université Catholique de Louvain

Modern personalised medicine uses high dimensional genomic data to perform customised diagnostic/prognostic. In addition, physicians record several medical parameters to evaluate some clinical status. In… (more)

Subjects/Keywords: Machine learning; Feature selection; Tree ensembles; Heterogeneous data; Kernel methods

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APA (6th Edition):

Paul, J. (2015). Feature selection from heterogeneous biomedical data. (Thesis). Université Catholique de Louvain. Retrieved from http://hdl.handle.net/2078.1/165076

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):

Paul, Jérôme. “Feature selection from heterogeneous biomedical data.” 2015. Thesis, Université Catholique de Louvain. Accessed October 31, 2020. http://hdl.handle.net/2078.1/165076.

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

MLA Handbook (7th Edition):

Paul, Jérôme. “Feature selection from heterogeneous biomedical data.” 2015. Web. 31 Oct 2020.

Vancouver:

Paul J. Feature selection from heterogeneous biomedical data. [Internet] [Thesis]. Université Catholique de Louvain; 2015. [cited 2020 Oct 31]. Available from: http://hdl.handle.net/2078.1/165076.

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

Council of Science Editors:

Paul J. Feature selection from heterogeneous biomedical data. [Thesis]. Université Catholique de Louvain; 2015. Available from: http://hdl.handle.net/2078.1/165076

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


Michigan State University

24. He, Tao, Ph. D. Kernel-based nonparametric testing in high-dimensional data with applications to gene set analysis.

Degree: 2015, Michigan State University

Thesis Ph. D. Michigan State University. Statistics 2015

The ultimate goal of genome-wide association studies (GWAS) is understanding the underlying relationship between genetic variants and… (more)

Subjects/Keywords: Genetics – Simulation methods; Kernel functions; Nonparametric statistics; Statistics; Biostatistics

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

APA (6th Edition):

He, Tao, P. D. (2015). Kernel-based nonparametric testing in high-dimensional data with applications to gene set analysis. (Thesis). Michigan State University. Retrieved from http://etd.lib.msu.edu/islandora/object/etd:3667

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):

He, Tao, Ph D. “Kernel-based nonparametric testing in high-dimensional data with applications to gene set analysis.” 2015. Thesis, Michigan State University. Accessed October 31, 2020. http://etd.lib.msu.edu/islandora/object/etd:3667.

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

MLA Handbook (7th Edition):

He, Tao, Ph D. “Kernel-based nonparametric testing in high-dimensional data with applications to gene set analysis.” 2015. Web. 31 Oct 2020.

Vancouver:

He, Tao PD. Kernel-based nonparametric testing in high-dimensional data with applications to gene set analysis. [Internet] [Thesis]. Michigan State University; 2015. [cited 2020 Oct 31]. Available from: http://etd.lib.msu.edu/islandora/object/etd:3667.

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

Council of Science Editors:

He, Tao PD. Kernel-based nonparametric testing in high-dimensional data with applications to gene set analysis. [Thesis]. Michigan State University; 2015. Available from: http://etd.lib.msu.edu/islandora/object/etd:3667

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

25. Ong, Cheng Soon. Kernels: Regularization and Optimization .

Degree: 2005, Australian National University

 This thesis extends the paradigm of machine learning with kernels. This paradigm is based on the idea of generalizing an inner product between vectors to… (more)

Subjects/Keywords: machine learning; kernel methods

…measure between objects. The kernel implicitly defines a feature mapping between the space of… …objects and the space of functions, called the reproducing kernel Hilbert space. There have been… …algorithms for optimizing the resulting problems. Since the kernel has to effectively capture the… …domain knowledge in an application, we study the problem of learning the kernel itself from… …training data. The proposed solution is a kernel on the space of kernels itself, which we called… 

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

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APA (6th Edition):

Ong, C. S. (2005). Kernels: Regularization and Optimization . (Thesis). Australian National University. Retrieved from http://hdl.handle.net/1885/7104

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):

Ong, Cheng Soon. “Kernels: Regularization and Optimization .” 2005. Thesis, Australian National University. Accessed October 31, 2020. http://hdl.handle.net/1885/7104.

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

MLA Handbook (7th Edition):

Ong, Cheng Soon. “Kernels: Regularization and Optimization .” 2005. Web. 31 Oct 2020.

Vancouver:

Ong CS. Kernels: Regularization and Optimization . [Internet] [Thesis]. Australian National University; 2005. [cited 2020 Oct 31]. Available from: http://hdl.handle.net/1885/7104.

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

Council of Science Editors:

Ong CS. Kernels: Regularization and Optimization . [Thesis]. Australian National University; 2005. Available from: http://hdl.handle.net/1885/7104

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


Delft University of Technology

26. Viering, T.J. (author). Active Learning by Discrepancy Minimization: A Comparison of Active Learning Methods Motivated by Generalization Bounds.

Degree: 2016, Delft University of Technology

In many settings in practice it is expensive to obtain labeled data while unlabeled data is abundant. This is problematic if one wants to train… (more)

Subjects/Keywords: active learning; machine learning; kernel methods; learning theory

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APA (6th Edition):

Viering, T. J. (. (2016). Active Learning by Discrepancy Minimization: A Comparison of Active Learning Methods Motivated by Generalization Bounds. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:cc9b668e-df22-43ae-8154-720ffaa8a1a4

Chicago Manual of Style (16th Edition):

Viering, T J (author). “Active Learning by Discrepancy Minimization: A Comparison of Active Learning Methods Motivated by Generalization Bounds.” 2016. Masters Thesis, Delft University of Technology. Accessed October 31, 2020. http://resolver.tudelft.nl/uuid:cc9b668e-df22-43ae-8154-720ffaa8a1a4.

MLA Handbook (7th Edition):

Viering, T J (author). “Active Learning by Discrepancy Minimization: A Comparison of Active Learning Methods Motivated by Generalization Bounds.” 2016. Web. 31 Oct 2020.

Vancouver:

Viering TJ(. Active Learning by Discrepancy Minimization: A Comparison of Active Learning Methods Motivated by Generalization Bounds. [Internet] [Masters thesis]. Delft University of Technology; 2016. [cited 2020 Oct 31]. Available from: http://resolver.tudelft.nl/uuid:cc9b668e-df22-43ae-8154-720ffaa8a1a4.

Council of Science Editors:

Viering TJ(. Active Learning by Discrepancy Minimization: A Comparison of Active Learning Methods Motivated by Generalization Bounds. [Masters Thesis]. Delft University of Technology; 2016. Available from: http://resolver.tudelft.nl/uuid:cc9b668e-df22-43ae-8154-720ffaa8a1a4


University of Texas – Austin

27. Yu, Chen-Han, Ph. D. The science of high performance algorithms for hierarchical matrices.

Degree: PhD, Computer Science, 2018, University of Texas – Austin

 Many matrices in scientific computing, statistical inference, and machine learning exhibit sparse and low-rank structure. Typically, such structure is exposed by appropriate matrix permutation of… (more)

Subjects/Keywords: Hierarchical matrices; Fast multipole methods; Kernel methods; Parallel algorithms; High-performance computing

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APA (6th Edition):

Yu, Chen-Han, P. D. (2018). The science of high performance algorithms for hierarchical matrices. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/68459

Chicago Manual of Style (16th Edition):

Yu, Chen-Han, Ph D. “The science of high performance algorithms for hierarchical matrices.” 2018. Doctoral Dissertation, University of Texas – Austin. Accessed October 31, 2020. http://hdl.handle.net/2152/68459.

MLA Handbook (7th Edition):

Yu, Chen-Han, Ph D. “The science of high performance algorithms for hierarchical matrices.” 2018. Web. 31 Oct 2020.

Vancouver:

Yu, Chen-Han PD. The science of high performance algorithms for hierarchical matrices. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2018. [cited 2020 Oct 31]. Available from: http://hdl.handle.net/2152/68459.

Council of Science Editors:

Yu, Chen-Han PD. The science of high performance algorithms for hierarchical matrices. [Doctoral Dissertation]. University of Texas – Austin; 2018. Available from: http://hdl.handle.net/2152/68459


University of Illinois – Chicago

28. Mahdav, Ashkan. Meshfree Methods for Fracture and High-Velocity Impact Simulation.

Degree: 2019, University of Illinois – Chicago

 The study of projectile penetration has a long history with a great military research interest for various applications such as projectile’s design to maximize depth… (more)

Subjects/Keywords: Meshfree methods; Reproducing Kernel Collocation; Fracture; Phase-field model; Semi-Lagrangian Reproducing Kernel; Penetration; High-Velocity Impact

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

APA (6th Edition):

Mahdav, A. (2019). Meshfree Methods for Fracture and High-Velocity Impact Simulation. (Thesis). University of Illinois – Chicago. Retrieved from http://hdl.handle.net/10027/23757

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):

Mahdav, Ashkan. “Meshfree Methods for Fracture and High-Velocity Impact Simulation.” 2019. Thesis, University of Illinois – Chicago. Accessed October 31, 2020. http://hdl.handle.net/10027/23757.

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

MLA Handbook (7th Edition):

Mahdav, Ashkan. “Meshfree Methods for Fracture and High-Velocity Impact Simulation.” 2019. Web. 31 Oct 2020.

Vancouver:

Mahdav A. Meshfree Methods for Fracture and High-Velocity Impact Simulation. [Internet] [Thesis]. University of Illinois – Chicago; 2019. [cited 2020 Oct 31]. Available from: http://hdl.handle.net/10027/23757.

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

Council of Science Editors:

Mahdav A. Meshfree Methods for Fracture and High-Velocity Impact Simulation. [Thesis]. University of Illinois – Chicago; 2019. Available from: http://hdl.handle.net/10027/23757

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


KTH

29. Wynen, Daan. Convolutional Kernel Networks for Action Recognition in Videos.

Degree: Computer Science and Communication (CSC), 2015, KTH

  While convolutional neural networks (CNNs) have taken the lead for many learning tasks, action recognition in videos has yet to see this jump in… (more)

Subjects/Keywords: Convolutional Kernel Networks; Convolutional Neural Networks; Kernel Methods; Action Recognition; Computer Vision; Video; Computer Sciences; Datavetenskap (datalogi)

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

APA (6th Edition):

Wynen, D. (2015). Convolutional Kernel Networks for Action Recognition in Videos. (Thesis). KTH. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-175797

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):

Wynen, Daan. “Convolutional Kernel Networks for Action Recognition in Videos.” 2015. Thesis, KTH. Accessed October 31, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-175797.

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

MLA Handbook (7th Edition):

Wynen, Daan. “Convolutional Kernel Networks for Action Recognition in Videos.” 2015. Web. 31 Oct 2020.

Vancouver:

Wynen D. Convolutional Kernel Networks for Action Recognition in Videos. [Internet] [Thesis]. KTH; 2015. [cited 2020 Oct 31]. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-175797.

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

Council of Science Editors:

Wynen D. Convolutional Kernel Networks for Action Recognition in Videos. [Thesis]. KTH; 2015. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-175797

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


University of Southern California

30. Gong, Boqing. Kernel methods for unsupervised domain adaptation.

Degree: PhD, Computer Science, 2015, University of Southern California

 In many applications (computer vision, natural language processing, speech recognition, etc.), the curse of domain mismatch arises when the test data (of a target domain)… (more)

Subjects/Keywords: kernel methods; domain adaptation; geodesic flow kernel; landmarks; rank of domains; latent domains; sequential determinantal point process

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

APA (6th Edition):

Gong, B. (2015). Kernel methods for unsupervised domain adaptation. (Doctoral Dissertation). University of Southern California. Retrieved from http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/604358/rec/3708

Chicago Manual of Style (16th Edition):

Gong, Boqing. “Kernel methods for unsupervised domain adaptation.” 2015. Doctoral Dissertation, University of Southern California. Accessed October 31, 2020. http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/604358/rec/3708.

MLA Handbook (7th Edition):

Gong, Boqing. “Kernel methods for unsupervised domain adaptation.” 2015. Web. 31 Oct 2020.

Vancouver:

Gong B. Kernel methods for unsupervised domain adaptation. [Internet] [Doctoral dissertation]. University of Southern California; 2015. [cited 2020 Oct 31]. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/604358/rec/3708.

Council of Science Editors:

Gong B. Kernel methods for unsupervised domain adaptation. [Doctoral Dissertation]. University of Southern California; 2015. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/604358/rec/3708

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