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You searched for subject:(dimension reduction). Showing records 1 – 30 of 169 total matches.

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Oregon State University

1. Thangavelu, Madan Kumar. On error bounds for linear feature extraction.

Degree: MS, Computer Science, 2010, Oregon State University

 Linear transformation for dimension reduction is a well established problem in the field of machine learning. Due to the numerous observability of parameters and data,… (more)

Subjects/Keywords: Dimension reduction; Dimension reduction (Statistics)

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APA (6th Edition):

Thangavelu, M. K. (2010). On error bounds for linear feature extraction. (Masters Thesis). Oregon State University. Retrieved from http://hdl.handle.net/1957/13886

Chicago Manual of Style (16th Edition):

Thangavelu, Madan Kumar. “On error bounds for linear feature extraction.” 2010. Masters Thesis, Oregon State University. Accessed February 20, 2018. http://hdl.handle.net/1957/13886.

MLA Handbook (7th Edition):

Thangavelu, Madan Kumar. “On error bounds for linear feature extraction.” 2010. Web. 20 Feb 2018.

Vancouver:

Thangavelu MK. On error bounds for linear feature extraction. [Internet] [Masters thesis]. Oregon State University; 2010. [cited 2018 Feb 20]. Available from: http://hdl.handle.net/1957/13886.

Council of Science Editors:

Thangavelu MK. On error bounds for linear feature extraction. [Masters Thesis]. Oregon State University; 2010. Available from: http://hdl.handle.net/1957/13886


University of Georgia

2. Wang, Qin. Sufficient dimension reduction and sufficient variable selection.

Degree: PhD, Statistics, 2009, University of Georgia

 The development in theory and methodology for sufficient dimension reduction has provided a powerful tool to tackle the challenging problem of high dimensional data analysis.… (more)

Subjects/Keywords: sufficient dimension reduction

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APA (6th Edition):

Wang, Q. (2009). Sufficient dimension reduction and sufficient variable selection. (Doctoral Dissertation). University of Georgia. Retrieved from http://purl.galileo.usg.edu/uga_etd/wang_qin_200905_phd

Chicago Manual of Style (16th Edition):

Wang, Qin. “Sufficient dimension reduction and sufficient variable selection.” 2009. Doctoral Dissertation, University of Georgia. Accessed February 20, 2018. http://purl.galileo.usg.edu/uga_etd/wang_qin_200905_phd.

MLA Handbook (7th Edition):

Wang, Qin. “Sufficient dimension reduction and sufficient variable selection.” 2009. Web. 20 Feb 2018.

Vancouver:

Wang Q. Sufficient dimension reduction and sufficient variable selection. [Internet] [Doctoral dissertation]. University of Georgia; 2009. [cited 2018 Feb 20]. Available from: http://purl.galileo.usg.edu/uga_etd/wang_qin_200905_phd.

Council of Science Editors:

Wang Q. Sufficient dimension reduction and sufficient variable selection. [Doctoral Dissertation]. University of Georgia; 2009. Available from: http://purl.galileo.usg.edu/uga_etd/wang_qin_200905_phd


Baylor University

3. [No author]. Three applications of linear dimension reduction.

Degree: 2017, Baylor University

 Linear Dimension Reduction (LDR) has many uses in engineering, business, medicine, economics, data science and others. LDR can be employed when observations are recorded with… (more)

Subjects/Keywords: Linear dimension reduction.

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APA (6th Edition):

author], [. (2017). Three applications of linear dimension reduction. (Thesis). Baylor University. Retrieved from http://hdl.handle.net/2104/10182

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):

author], [No. “Three applications of linear dimension reduction. ” 2017. Thesis, Baylor University. Accessed February 20, 2018. http://hdl.handle.net/2104/10182.

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

MLA Handbook (7th Edition):

author], [No. “Three applications of linear dimension reduction. ” 2017. Web. 20 Feb 2018.

Vancouver:

author] [. Three applications of linear dimension reduction. [Internet] [Thesis]. Baylor University; 2017. [cited 2018 Feb 20]. Available from: http://hdl.handle.net/2104/10182.

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

Council of Science Editors:

author] [. Three applications of linear dimension reduction. [Thesis]. Baylor University; 2017. Available from: http://hdl.handle.net/2104/10182

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


University of Georgia

4. Ling, Yangrong. Statistical dimension reduction methods for appearance-based face recognition.

Degree: MS, Computer Science, 2003, University of Georgia

 Two novel moment-based methods which are insensitive to large variation in lighting direction and facial expression are developed for appearance-based face recognition using dimension reduction(more)

Subjects/Keywords: Dimension-reduction

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APA (6th Edition):

Ling, Y. (2003). Statistical dimension reduction methods for appearance-based face recognition. (Masters Thesis). University of Georgia. Retrieved from http://purl.galileo.usg.edu/uga_etd/ling_yangrong_200305_ms

Chicago Manual of Style (16th Edition):

Ling, Yangrong. “Statistical dimension reduction methods for appearance-based face recognition.” 2003. Masters Thesis, University of Georgia. Accessed February 20, 2018. http://purl.galileo.usg.edu/uga_etd/ling_yangrong_200305_ms.

MLA Handbook (7th Edition):

Ling, Yangrong. “Statistical dimension reduction methods for appearance-based face recognition.” 2003. Web. 20 Feb 2018.

Vancouver:

Ling Y. Statistical dimension reduction methods for appearance-based face recognition. [Internet] [Masters thesis]. University of Georgia; 2003. [cited 2018 Feb 20]. Available from: http://purl.galileo.usg.edu/uga_etd/ling_yangrong_200305_ms.

Council of Science Editors:

Ling Y. Statistical dimension reduction methods for appearance-based face recognition. [Masters Thesis]. University of Georgia; 2003. Available from: http://purl.galileo.usg.edu/uga_etd/ling_yangrong_200305_ms


Baylor University

5. Young, Phil D. Topics in dimension reduction and missing data in statistical discrimination.

Degree: Statistical Sciences., 2010, Baylor University

 This dissertation is comprised of four chapters. In the first chapter, we define the concept of linear dimension reduction, review some popular linear dimension reduction(more)

Subjects/Keywords: Dimension reduction.; Statistical discrimination.

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APA (6th Edition):

Young, P. D. (2010). Topics in dimension reduction and missing data in statistical discrimination. (Thesis). Baylor University. Retrieved from http://hdl.handle.net/2104/5543

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):

Young, Phil D. “Topics in dimension reduction and missing data in statistical discrimination. ” 2010. Thesis, Baylor University. Accessed February 20, 2018. http://hdl.handle.net/2104/5543.

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

MLA Handbook (7th Edition):

Young, Phil D. “Topics in dimension reduction and missing data in statistical discrimination. ” 2010. Web. 20 Feb 2018.

Vancouver:

Young PD. Topics in dimension reduction and missing data in statistical discrimination. [Internet] [Thesis]. Baylor University; 2010. [cited 2018 Feb 20]. Available from: http://hdl.handle.net/2104/5543.

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

Council of Science Editors:

Young PD. Topics in dimension reduction and missing data in statistical discrimination. [Thesis]. Baylor University; 2010. Available from: http://hdl.handle.net/2104/5543

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


University of Johannesburg

6. Coulter, Duncan Anthony. Immunologically amplified knowledge and intentions dimensionality reduction in cooperative multi-agent systems .

Degree: 2014, University of Johannesburg

 The development of software systems is a relatively recent field of human endeavour. Even so, it has followed a steady progression of dominant paradigms which… (more)

Subjects/Keywords: Dimension reduction (Statistics); Multiagent systems

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APA (6th Edition):

Coulter, D. A. (2014). Immunologically amplified knowledge and intentions dimensionality reduction in cooperative multi-agent systems . (Thesis). University of Johannesburg. Retrieved from http://hdl.handle.net/10210/12341

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):

Coulter, Duncan Anthony. “Immunologically amplified knowledge and intentions dimensionality reduction in cooperative multi-agent systems .” 2014. Thesis, University of Johannesburg. Accessed February 20, 2018. http://hdl.handle.net/10210/12341.

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

MLA Handbook (7th Edition):

Coulter, Duncan Anthony. “Immunologically amplified knowledge and intentions dimensionality reduction in cooperative multi-agent systems .” 2014. Web. 20 Feb 2018.

Vancouver:

Coulter DA. Immunologically amplified knowledge and intentions dimensionality reduction in cooperative multi-agent systems . [Internet] [Thesis]. University of Johannesburg; 2014. [cited 2018 Feb 20]. Available from: http://hdl.handle.net/10210/12341.

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

Council of Science Editors:

Coulter DA. Immunologically amplified knowledge and intentions dimensionality reduction in cooperative multi-agent systems . [Thesis]. University of Johannesburg; 2014. Available from: http://hdl.handle.net/10210/12341

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


Penn State University

7. Wang, Yu. Nonlinear Dimension Reduction in Feature Space.

Degree: PhD, Statistics, 2008, Penn State University

 In this thesis I introduce an idea for applying dimension reduction methods to feature spaces. Three main methods will be used to estimate dimension reduction(more)

Subjects/Keywords: Dimension Reduction; Feature Space

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APA (6th Edition):

Wang, Y. (2008). Nonlinear Dimension Reduction in Feature Space. (Doctoral Dissertation). Penn State University. Retrieved from https://etda.libraries.psu.edu/catalog/8637

Chicago Manual of Style (16th Edition):

Wang, Yu. “Nonlinear Dimension Reduction in Feature Space.” 2008. Doctoral Dissertation, Penn State University. Accessed February 20, 2018. https://etda.libraries.psu.edu/catalog/8637.

MLA Handbook (7th Edition):

Wang, Yu. “Nonlinear Dimension Reduction in Feature Space.” 2008. Web. 20 Feb 2018.

Vancouver:

Wang Y. Nonlinear Dimension Reduction in Feature Space. [Internet] [Doctoral dissertation]. Penn State University; 2008. [cited 2018 Feb 20]. Available from: https://etda.libraries.psu.edu/catalog/8637.

Council of Science Editors:

Wang Y. Nonlinear Dimension Reduction in Feature Space. [Doctoral Dissertation]. Penn State University; 2008. Available from: https://etda.libraries.psu.edu/catalog/8637


Clemson University

8. Knoll, Fiona. Johnson-Lindenstrauss Transformations.

Degree: PhD, Mathematical Sciences, 2017, Clemson University

 With the quick progression of technology and the increasing need to process large data, there has been an increased interest in data-dependent and data-independent dimension(more)

Subjects/Keywords: Data; Dimension Reduction; Johnson-Lindenstrauss

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APA (6th Edition):

Knoll, F. (2017). Johnson-Lindenstrauss Transformations. (Doctoral Dissertation). Clemson University. Retrieved from https://tigerprints.clemson.edu/all_dissertations/1977

Chicago Manual of Style (16th Edition):

Knoll, Fiona. “Johnson-Lindenstrauss Transformations.” 2017. Doctoral Dissertation, Clemson University. Accessed February 20, 2018. https://tigerprints.clemson.edu/all_dissertations/1977.

MLA Handbook (7th Edition):

Knoll, Fiona. “Johnson-Lindenstrauss Transformations.” 2017. Web. 20 Feb 2018.

Vancouver:

Knoll F. Johnson-Lindenstrauss Transformations. [Internet] [Doctoral dissertation]. Clemson University; 2017. [cited 2018 Feb 20]. Available from: https://tigerprints.clemson.edu/all_dissertations/1977.

Council of Science Editors:

Knoll F. Johnson-Lindenstrauss Transformations. [Doctoral Dissertation]. Clemson University; 2017. Available from: https://tigerprints.clemson.edu/all_dissertations/1977


University of Waterloo

9. Liu, Kai. Effective Dimensionality Control in Quantitative Finance and Insurance.

Degree: 2017, University of Waterloo

 It is well-known that dimension reduction techniques such as the Brownian bridge, principal component analysis, linear transformation could increase the efficiency of Quasi-Monte Carlo (QMC)… (more)

Subjects/Keywords: QMC; Dimension Reduction; Effective Dimension; Effective Portfolio; Effective Portfolio Dimension

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APA (6th Edition):

Liu, K. (2017). Effective Dimensionality Control in Quantitative Finance and Insurance. (Thesis). University of Waterloo. Retrieved from http://hdl.handle.net/10012/12324

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, Kai. “Effective Dimensionality Control in Quantitative Finance and Insurance.” 2017. Thesis, University of Waterloo. Accessed February 20, 2018. http://hdl.handle.net/10012/12324.

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

MLA Handbook (7th Edition):

Liu, Kai. “Effective Dimensionality Control in Quantitative Finance and Insurance.” 2017. Web. 20 Feb 2018.

Vancouver:

Liu K. Effective Dimensionality Control in Quantitative Finance and Insurance. [Internet] [Thesis]. University of Waterloo; 2017. [cited 2018 Feb 20]. Available from: http://hdl.handle.net/10012/12324.

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

Council of Science Editors:

Liu K. Effective Dimensionality Control in Quantitative Finance and Insurance. [Thesis]. University of Waterloo; 2017. Available from: http://hdl.handle.net/10012/12324

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


Temple University

10. Yang, Chaozheng. Sufficient Dimension Reduction in Complex Datasets.

Degree: PhD, 2016, Temple University

Statistics

This dissertation focuses on two problems in dimension reduction. One is using permutation approach to test predictor contribution. The permutation approach applies to marginal… (more)

Subjects/Keywords: Statistics;

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APA (6th Edition):

Yang, C. (2016). Sufficient Dimension Reduction in Complex Datasets. (Doctoral Dissertation). Temple University. Retrieved from http://digital.library.temple.edu/u?/p245801coll10,404627

Chicago Manual of Style (16th Edition):

Yang, Chaozheng. “Sufficient Dimension Reduction in Complex Datasets.” 2016. Doctoral Dissertation, Temple University. Accessed February 20, 2018. http://digital.library.temple.edu/u?/p245801coll10,404627.

MLA Handbook (7th Edition):

Yang, Chaozheng. “Sufficient Dimension Reduction in Complex Datasets.” 2016. Web. 20 Feb 2018.

Vancouver:

Yang C. Sufficient Dimension Reduction in Complex Datasets. [Internet] [Doctoral dissertation]. Temple University; 2016. [cited 2018 Feb 20]. Available from: http://digital.library.temple.edu/u?/p245801coll10,404627.

Council of Science Editors:

Yang C. Sufficient Dimension Reduction in Complex Datasets. [Doctoral Dissertation]. Temple University; 2016. Available from: http://digital.library.temple.edu/u?/p245801coll10,404627


University of Minnesota

11. Chen, Xin. Sufficient dimension reduction and variable selection.

Degree: PhD, Statistics, 2010, University of Minnesota

 Sufficient dimension reduction (SDR) in regression was first introduced by Cook (2004). It reduces the dimension of the predictor space without loss of information and… (more)

Subjects/Keywords: Central subspace; Dimension reduction; Regression; Sufficient dimension reduction; Variable selection; Statistics

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APA (6th Edition):

Chen, X. (2010). Sufficient dimension reduction and variable selection. (Doctoral Dissertation). University of Minnesota. Retrieved from http://purl.umn.edu/99484

Chicago Manual of Style (16th Edition):

Chen, Xin. “Sufficient dimension reduction and variable selection.” 2010. Doctoral Dissertation, University of Minnesota. Accessed February 20, 2018. http://purl.umn.edu/99484.

MLA Handbook (7th Edition):

Chen, Xin. “Sufficient dimension reduction and variable selection.” 2010. Web. 20 Feb 2018.

Vancouver:

Chen X. Sufficient dimension reduction and variable selection. [Internet] [Doctoral dissertation]. University of Minnesota; 2010. [cited 2018 Feb 20]. Available from: http://purl.umn.edu/99484.

Council of Science Editors:

Chen X. Sufficient dimension reduction and variable selection. [Doctoral Dissertation]. University of Minnesota; 2010. Available from: http://purl.umn.edu/99484


University of Technology, Sydney

12. Bian, Wei. Supervised linear dimension reduction.

Degree: 2012, University of Technology, Sydney

 Supervised linear dimension reduction (SLDR) is one of the most effective methods for complexity reduction, which has been widely applied in pattern recognition, computer vision,… (more)

Subjects/Keywords: Pattern recognition.; Dimension reduction.; Statistics.; Mathematics.

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APA (6th Edition):

Bian, W. (2012). Supervised linear dimension reduction. (Thesis). University of Technology, Sydney. Retrieved from http://hdl.handle.net/10453/20422

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):

Bian, Wei. “Supervised linear dimension reduction.” 2012. Thesis, University of Technology, Sydney. Accessed February 20, 2018. http://hdl.handle.net/10453/20422.

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

MLA Handbook (7th Edition):

Bian, Wei. “Supervised linear dimension reduction.” 2012. Web. 20 Feb 2018.

Vancouver:

Bian W. Supervised linear dimension reduction. [Internet] [Thesis]. University of Technology, Sydney; 2012. [cited 2018 Feb 20]. Available from: http://hdl.handle.net/10453/20422.

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

Council of Science Editors:

Bian W. Supervised linear dimension reduction. [Thesis]. University of Technology, Sydney; 2012. Available from: http://hdl.handle.net/10453/20422

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


Cornell University

13. Chen, Maximillian. Dimension Reduction And Inferential Procedures For Images .

Degree: 2014, Cornell University

 High-dimensional data analysis has been a prominent topic of statistical research in recent years due to the growing presence of high-dimensional electronic data. Much of… (more)

Subjects/Keywords: imaging data; dimension reduction; hypothesis testing

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APA (6th Edition):

Chen, M. (2014). Dimension Reduction And Inferential Procedures For Images . (Thesis). Cornell University. Retrieved from http://hdl.handle.net/1813/37105

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):

Chen, Maximillian. “Dimension Reduction And Inferential Procedures For Images .” 2014. Thesis, Cornell University. Accessed February 20, 2018. http://hdl.handle.net/1813/37105.

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

MLA Handbook (7th Edition):

Chen, Maximillian. “Dimension Reduction And Inferential Procedures For Images .” 2014. Web. 20 Feb 2018.

Vancouver:

Chen M. Dimension Reduction And Inferential Procedures For Images . [Internet] [Thesis]. Cornell University; 2014. [cited 2018 Feb 20]. Available from: http://hdl.handle.net/1813/37105.

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

Council of Science Editors:

Chen M. Dimension Reduction And Inferential Procedures For Images . [Thesis]. Cornell University; 2014. Available from: http://hdl.handle.net/1813/37105

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


Uppsala University

14. Li, Qiongzhu. Study of Single and Ensemble Machine Learning Models on Credit Data to Detect Underlying Non-performing Loans.

Degree: Statistics, 2016, Uppsala University

  In this paper, we try to compare the performance of two feature dimension reduction methods, the LASSO and PCA. Both simulation study and empirical… (more)

Subjects/Keywords: Machine learning; Feature Dimension Reduction; NPL

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APA (6th Edition):

Li, Q. (2016). Study of Single and Ensemble Machine Learning Models on Credit Data to Detect Underlying Non-performing Loans. (Thesis). Uppsala University. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-297080

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):

Li, Qiongzhu. “Study of Single and Ensemble Machine Learning Models on Credit Data to Detect Underlying Non-performing Loans.” 2016. Thesis, Uppsala University. Accessed February 20, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-297080.

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

MLA Handbook (7th Edition):

Li, Qiongzhu. “Study of Single and Ensemble Machine Learning Models on Credit Data to Detect Underlying Non-performing Loans.” 2016. Web. 20 Feb 2018.

Vancouver:

Li Q. Study of Single and Ensemble Machine Learning Models on Credit Data to Detect Underlying Non-performing Loans. [Internet] [Thesis]. Uppsala University; 2016. [cited 2018 Feb 20]. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-297080.

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

Council of Science Editors:

Li Q. Study of Single and Ensemble Machine Learning Models on Credit Data to Detect Underlying Non-performing Loans. [Thesis]. Uppsala University; 2016. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-297080

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


University of Minnesota

15. Adragni, Kofi Placid. Dimension reduction and prediction in large p regressions.

Degree: PhD, Statistics, 2009, University of Minnesota

 A high dimensional regression setting is considered with p predictors X=(X1,...,Xp)T and a response Y. The interest is with large p, possibly much larger than… (more)

Subjects/Keywords: Dimension Reduction; Prediction; Principal Components; Principal Fitted Components; Regression; Sufficient Dimension Reduction; Statistics

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APA (6th Edition):

Adragni, K. P. (2009). Dimension reduction and prediction in large p regressions. (Doctoral Dissertation). University of Minnesota. Retrieved from http://purl.umn.edu/51904

Chicago Manual of Style (16th Edition):

Adragni, Kofi Placid. “Dimension reduction and prediction in large p regressions.” 2009. Doctoral Dissertation, University of Minnesota. Accessed February 20, 2018. http://purl.umn.edu/51904.

MLA Handbook (7th Edition):

Adragni, Kofi Placid. “Dimension reduction and prediction in large p regressions.” 2009. Web. 20 Feb 2018.

Vancouver:

Adragni KP. Dimension reduction and prediction in large p regressions. [Internet] [Doctoral dissertation]. University of Minnesota; 2009. [cited 2018 Feb 20]. Available from: http://purl.umn.edu/51904.

Council of Science Editors:

Adragni KP. Dimension reduction and prediction in large p regressions. [Doctoral Dissertation]. University of Minnesota; 2009. Available from: http://purl.umn.edu/51904

16. Hoyos-Idrobo, Andrés. Ensembles des modeles en fMRI : l'apprentissage stable à grande échelle : Ensembles of models in fMRI : stable learning in large-scale settings.

Degree: Docteur es, Informatique, 2017, Paris Saclay

En imagerie médicale, des collaborations internationales ont lançé l'acquisition de centaines de Terabytes de données - et en particulierde données d'Imagerie par Résonance Magnétique fonctionelle… (more)

Subjects/Keywords: IRMf; Clustering; Reduction de dimension; Décodage; FMRI; Clustering; Dimentionality reduction; Decoding

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APA (6th Edition):

Hoyos-Idrobo, A. (2017). Ensembles des modeles en fMRI : l'apprentissage stable à grande échelle : Ensembles of models in fMRI : stable learning in large-scale settings. (Doctoral Dissertation). Paris Saclay. Retrieved from http://www.theses.fr/2017SACLS029

Chicago Manual of Style (16th Edition):

Hoyos-Idrobo, Andrés. “Ensembles des modeles en fMRI : l'apprentissage stable à grande échelle : Ensembles of models in fMRI : stable learning in large-scale settings.” 2017. Doctoral Dissertation, Paris Saclay. Accessed February 20, 2018. http://www.theses.fr/2017SACLS029.

MLA Handbook (7th Edition):

Hoyos-Idrobo, Andrés. “Ensembles des modeles en fMRI : l'apprentissage stable à grande échelle : Ensembles of models in fMRI : stable learning in large-scale settings.” 2017. Web. 20 Feb 2018.

Vancouver:

Hoyos-Idrobo A. Ensembles des modeles en fMRI : l'apprentissage stable à grande échelle : Ensembles of models in fMRI : stable learning in large-scale settings. [Internet] [Doctoral dissertation]. Paris Saclay; 2017. [cited 2018 Feb 20]. Available from: http://www.theses.fr/2017SACLS029.

Council of Science Editors:

Hoyos-Idrobo A. Ensembles des modeles en fMRI : l'apprentissage stable à grande échelle : Ensembles of models in fMRI : stable learning in large-scale settings. [Doctoral Dissertation]. Paris Saclay; 2017. Available from: http://www.theses.fr/2017SACLS029


Clemson University

17. Wilson, Matthew Robert. Comparison of Karnin Sensitivity and Principal Component Analysis in Reducing Input Dimensionality.

Degree: MS, Computer Engineering, 2016, Clemson University

 Reducing the input dimensionality of large datasets for subsequent processing will allow the process to become less computationally complex and expensive. This thesis tests if… (more)

Subjects/Keywords: Dimension reduction; feature reduction; feature selection; input reduction; Karnin Sensitivity; Principal Component Analysis

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APA (6th Edition):

Wilson, M. R. (2016). Comparison of Karnin Sensitivity and Principal Component Analysis in Reducing Input Dimensionality. (Masters Thesis). Clemson University. Retrieved from https://tigerprints.clemson.edu/all_theses/2357

Chicago Manual of Style (16th Edition):

Wilson, Matthew Robert. “Comparison of Karnin Sensitivity and Principal Component Analysis in Reducing Input Dimensionality.” 2016. Masters Thesis, Clemson University. Accessed February 20, 2018. https://tigerprints.clemson.edu/all_theses/2357.

MLA Handbook (7th Edition):

Wilson, Matthew Robert. “Comparison of Karnin Sensitivity and Principal Component Analysis in Reducing Input Dimensionality.” 2016. Web. 20 Feb 2018.

Vancouver:

Wilson MR. Comparison of Karnin Sensitivity and Principal Component Analysis in Reducing Input Dimensionality. [Internet] [Masters thesis]. Clemson University; 2016. [cited 2018 Feb 20]. Available from: https://tigerprints.clemson.edu/all_theses/2357.

Council of Science Editors:

Wilson MR. Comparison of Karnin Sensitivity and Principal Component Analysis in Reducing Input Dimensionality. [Masters Thesis]. Clemson University; 2016. Available from: https://tigerprints.clemson.edu/all_theses/2357


University of Waterloo

18. Liu, Kai. Directional Control of Generating Brownian Path under Quasi Monte Carlo.

Degree: 2012, University of Waterloo

 Quasi-Monte Carlo (QMC) methods are playing an increasingly important role in computational finance. This is attributed to the increased complexity of the derivative securities and… (more)

Subjects/Keywords: QMC; Low Discrepancy Sequence; Effective Dimension; Dimension Reduction; PCA; BB; LT; OT; FOT; DC

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APA (6th Edition):

Liu, K. (2012). Directional Control of Generating Brownian Path under Quasi Monte Carlo. (Thesis). University of Waterloo. Retrieved from http://hdl.handle.net/10012/6984

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, Kai. “Directional Control of Generating Brownian Path under Quasi Monte Carlo.” 2012. Thesis, University of Waterloo. Accessed February 20, 2018. http://hdl.handle.net/10012/6984.

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

MLA Handbook (7th Edition):

Liu, Kai. “Directional Control of Generating Brownian Path under Quasi Monte Carlo.” 2012. Web. 20 Feb 2018.

Vancouver:

Liu K. Directional Control of Generating Brownian Path under Quasi Monte Carlo. [Internet] [Thesis]. University of Waterloo; 2012. [cited 2018 Feb 20]. Available from: http://hdl.handle.net/10012/6984.

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

Council of Science Editors:

Liu K. Directional Control of Generating Brownian Path under Quasi Monte Carlo. [Thesis]. University of Waterloo; 2012. Available from: http://hdl.handle.net/10012/6984

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

19. Lu, Weizhi. Contribution to dimension reduction techniques : application to object tracking : Contribution aux techniques de la réduction de dimension : application au suivi d'objet.

Degree: Docteur es, Traitement du signal et de l'image, 2014, Rennes, INSA

Cette thèse étudie et apporte des améliorations significatives sur trois techniques répandues en réduction de dimension : l'acquisition parcimonieuse (ou l'échantillonnage parcimonieux), la projection aléatoire… (more)

Subjects/Keywords: Réduction de dimension; Dimension reduction; Compressed sensing; Random projection; Sparse representation; 621.382

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APA (6th Edition):

Lu, W. (2014). Contribution to dimension reduction techniques : application to object tracking : Contribution aux techniques de la réduction de dimension : application au suivi d'objet. (Doctoral Dissertation). Rennes, INSA. Retrieved from http://www.theses.fr/2014ISAR0010

Chicago Manual of Style (16th Edition):

Lu, Weizhi. “Contribution to dimension reduction techniques : application to object tracking : Contribution aux techniques de la réduction de dimension : application au suivi d'objet.” 2014. Doctoral Dissertation, Rennes, INSA. Accessed February 20, 2018. http://www.theses.fr/2014ISAR0010.

MLA Handbook (7th Edition):

Lu, Weizhi. “Contribution to dimension reduction techniques : application to object tracking : Contribution aux techniques de la réduction de dimension : application au suivi d'objet.” 2014. Web. 20 Feb 2018.

Vancouver:

Lu W. Contribution to dimension reduction techniques : application to object tracking : Contribution aux techniques de la réduction de dimension : application au suivi d'objet. [Internet] [Doctoral dissertation]. Rennes, INSA; 2014. [cited 2018 Feb 20]. Available from: http://www.theses.fr/2014ISAR0010.

Council of Science Editors:

Lu W. Contribution to dimension reduction techniques : application to object tracking : Contribution aux techniques de la réduction de dimension : application au suivi d'objet. [Doctoral Dissertation]. Rennes, INSA; 2014. Available from: http://www.theses.fr/2014ISAR0010

20. Vu, Khac Ky. Random projection for high-dimensional optimization : Projection aléatoire pour l'optimisation de grande dimension.

Degree: Docteur es, Informatique, 2016, Paris Saclay

 À l'ère de la numérisation, les données devient pas cher et facile à obtenir. Cela se traduit par de nombreux nouveaux problèmes d'optimisation avec de… (more)

Subjects/Keywords: Réduction de dimension; Approximation; Optimisation; Algorithmes randomisés; Dimension reduction; Approximation; Optimization; Randomized algorithms

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

APA (6th Edition):

Vu, K. K. (2016). Random projection for high-dimensional optimization : Projection aléatoire pour l'optimisation de grande dimension. (Doctoral Dissertation). Paris Saclay. Retrieved from http://www.theses.fr/2016SACLX031

Chicago Manual of Style (16th Edition):

Vu, Khac Ky. “Random projection for high-dimensional optimization : Projection aléatoire pour l'optimisation de grande dimension.” 2016. Doctoral Dissertation, Paris Saclay. Accessed February 20, 2018. http://www.theses.fr/2016SACLX031.

MLA Handbook (7th Edition):

Vu, Khac Ky. “Random projection for high-dimensional optimization : Projection aléatoire pour l'optimisation de grande dimension.” 2016. Web. 20 Feb 2018.

Vancouver:

Vu KK. Random projection for high-dimensional optimization : Projection aléatoire pour l'optimisation de grande dimension. [Internet] [Doctoral dissertation]. Paris Saclay; 2016. [cited 2018 Feb 20]. Available from: http://www.theses.fr/2016SACLX031.

Council of Science Editors:

Vu KK. Random projection for high-dimensional optimization : Projection aléatoire pour l'optimisation de grande dimension. [Doctoral Dissertation]. Paris Saclay; 2016. Available from: http://www.theses.fr/2016SACLX031

21. Chiancone, Alessandro. Réduction de dimension via Sliced Inverse Regression : Idées et nouvelles propositions : Dimension reductio via Sliced Inverse Regression : ideas and extensions.

Degree: Docteur es, Mathématiques Appliquées, 2016, Grenoble Alpes

Cette thèse propose trois extensions de la Régression linéaire par tranches (Sliced Inverse Regression, SIR), notamment Collaborative SIR, Student SIR et Knockoff SIR.Une des faiblesses… (more)

Subjects/Keywords: Régression linéaire par tranches; Reduction de dimension; Selection de variables; Sliced Inverse Regression; Dimension reduction; Variable selection; 510

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APA (6th Edition):

Chiancone, A. (2016). Réduction de dimension via Sliced Inverse Regression : Idées et nouvelles propositions : Dimension reductio via Sliced Inverse Regression : ideas and extensions. (Doctoral Dissertation). Grenoble Alpes. Retrieved from http://www.theses.fr/2016GREAM051

Chicago Manual of Style (16th Edition):

Chiancone, Alessandro. “Réduction de dimension via Sliced Inverse Regression : Idées et nouvelles propositions : Dimension reductio via Sliced Inverse Regression : ideas and extensions.” 2016. Doctoral Dissertation, Grenoble Alpes. Accessed February 20, 2018. http://www.theses.fr/2016GREAM051.

MLA Handbook (7th Edition):

Chiancone, Alessandro. “Réduction de dimension via Sliced Inverse Regression : Idées et nouvelles propositions : Dimension reductio via Sliced Inverse Regression : ideas and extensions.” 2016. Web. 20 Feb 2018.

Vancouver:

Chiancone A. Réduction de dimension via Sliced Inverse Regression : Idées et nouvelles propositions : Dimension reductio via Sliced Inverse Regression : ideas and extensions. [Internet] [Doctoral dissertation]. Grenoble Alpes; 2016. [cited 2018 Feb 20]. Available from: http://www.theses.fr/2016GREAM051.

Council of Science Editors:

Chiancone A. Réduction de dimension via Sliced Inverse Regression : Idées et nouvelles propositions : Dimension reductio via Sliced Inverse Regression : ideas and extensions. [Doctoral Dissertation]. Grenoble Alpes; 2016. Available from: http://www.theses.fr/2016GREAM051


Texas State University – San Marcos

22. Reiss, Randolf H. EIGENVALUES AND EIGENVECTORS IN DATA DIMENSION REDUCTION FOR REGRESSION.

Degree: 2013, Texas State University – San Marcos

 A basic theory of eigenvalues and eigenvectors as a means to reduce the dimension of data, is presented. Iterative methods for finding eigenvalues and eigenvectors… (more)

Subjects/Keywords: Eigenvector, Eigenvalue, Dimension Reduction, Power Method, Partial Least Squares.; Eigenvalues; Eigenvectors; Data reduction

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

APA (6th Edition):

Reiss, R. H. (2013). EIGENVALUES AND EIGENVECTORS IN DATA DIMENSION REDUCTION FOR REGRESSION. (Thesis). Texas State University – San Marcos. Retrieved from https://digital.library.txstate.edu/handle/10877/4696

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):

Reiss, Randolf H. “EIGENVALUES AND EIGENVECTORS IN DATA DIMENSION REDUCTION FOR REGRESSION.” 2013. Thesis, Texas State University – San Marcos. Accessed February 20, 2018. https://digital.library.txstate.edu/handle/10877/4696.

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

MLA Handbook (7th Edition):

Reiss, Randolf H. “EIGENVALUES AND EIGENVECTORS IN DATA DIMENSION REDUCTION FOR REGRESSION.” 2013. Web. 20 Feb 2018.

Vancouver:

Reiss RH. EIGENVALUES AND EIGENVECTORS IN DATA DIMENSION REDUCTION FOR REGRESSION. [Internet] [Thesis]. Texas State University – San Marcos; 2013. [cited 2018 Feb 20]. Available from: https://digital.library.txstate.edu/handle/10877/4696.

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

Council of Science Editors:

Reiss RH. EIGENVALUES AND EIGENVECTORS IN DATA DIMENSION REDUCTION FOR REGRESSION. [Thesis]. Texas State University – San Marcos; 2013. Available from: https://digital.library.txstate.edu/handle/10877/4696

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


University of Arizona

23. Wauters, John. Independence Screening in High-Dimensional Data .

Degree: 2016, University of Arizona

 High-dimensional data, data in which the number of dimensions exceeds the number of observations, is increasingly common in statistics. The term "ultra-high dimensional" is defined… (more)

Subjects/Keywords: feature screening; high-dimensional data; independence screening; modeling; dimension reduction

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APA (6th Edition):

Wauters, J. (2016). Independence Screening in High-Dimensional Data . (Masters Thesis). University of Arizona. Retrieved from http://hdl.handle.net/10150/623083

Chicago Manual of Style (16th Edition):

Wauters, John. “Independence Screening in High-Dimensional Data .” 2016. Masters Thesis, University of Arizona. Accessed February 20, 2018. http://hdl.handle.net/10150/623083.

MLA Handbook (7th Edition):

Wauters, John. “Independence Screening in High-Dimensional Data .” 2016. Web. 20 Feb 2018.

Vancouver:

Wauters J. Independence Screening in High-Dimensional Data . [Internet] [Masters thesis]. University of Arizona; 2016. [cited 2018 Feb 20]. Available from: http://hdl.handle.net/10150/623083.

Council of Science Editors:

Wauters J. Independence Screening in High-Dimensional Data . [Masters Thesis]. University of Arizona; 2016. Available from: http://hdl.handle.net/10150/623083


Rochester Institute of Technology

24. Johnson, Juan Emmanuel. Schroedinger Eigenmaps for Manifold Alignment of Multimodal Hyperspectral Images.

Degree: MS, School of Mathematical Sciences (COS), 2016, Rochester Institute of Technology

  Multimodal remote sensing is an upcoming field as it allows for many views of the same region of interest. Domain adaption attempts to fuse… (more)

Subjects/Keywords: Computer vision; Data fusion; Dimension reduction; Image science; Remote sensing

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APA (6th Edition):

Johnson, J. E. (2016). Schroedinger Eigenmaps for Manifold Alignment of Multimodal Hyperspectral Images. (Masters Thesis). Rochester Institute of Technology. Retrieved from http://scholarworks.rit.edu/theses/9324

Chicago Manual of Style (16th Edition):

Johnson, Juan Emmanuel. “Schroedinger Eigenmaps for Manifold Alignment of Multimodal Hyperspectral Images.” 2016. Masters Thesis, Rochester Institute of Technology. Accessed February 20, 2018. http://scholarworks.rit.edu/theses/9324.

MLA Handbook (7th Edition):

Johnson, Juan Emmanuel. “Schroedinger Eigenmaps for Manifold Alignment of Multimodal Hyperspectral Images.” 2016. Web. 20 Feb 2018.

Vancouver:

Johnson JE. Schroedinger Eigenmaps for Manifold Alignment of Multimodal Hyperspectral Images. [Internet] [Masters thesis]. Rochester Institute of Technology; 2016. [cited 2018 Feb 20]. Available from: http://scholarworks.rit.edu/theses/9324.

Council of Science Editors:

Johnson JE. Schroedinger Eigenmaps for Manifold Alignment of Multimodal Hyperspectral Images. [Masters Thesis]. Rochester Institute of Technology; 2016. Available from: http://scholarworks.rit.edu/theses/9324


University of Guelph

25. Morris, Katherine. Dimension Reduction for Model-based Clustering via Mixtures of Multivariate t-Distributions .

Degree: 2012, University of Guelph

 We introduce a dimension reduction method for model-based clustering obtained from a finite mixture of t-distributions. This approach is based on existing work on reducing… (more)

Subjects/Keywords: mclust; tEIGEN; model-based clustering; dimension reduction; multivariate t-mixtures

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APA (6th Edition):

Morris, K. (2012). Dimension Reduction for Model-based Clustering via Mixtures of Multivariate t-Distributions . (Thesis). University of Guelph. Retrieved from https://atrium.lib.uoguelph.ca/xmlui/handle/10214/3863

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):

Morris, Katherine. “Dimension Reduction for Model-based Clustering via Mixtures of Multivariate t-Distributions .” 2012. Thesis, University of Guelph. Accessed February 20, 2018. https://atrium.lib.uoguelph.ca/xmlui/handle/10214/3863.

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

MLA Handbook (7th Edition):

Morris, Katherine. “Dimension Reduction for Model-based Clustering via Mixtures of Multivariate t-Distributions .” 2012. Web. 20 Feb 2018.

Vancouver:

Morris K. Dimension Reduction for Model-based Clustering via Mixtures of Multivariate t-Distributions . [Internet] [Thesis]. University of Guelph; 2012. [cited 2018 Feb 20]. Available from: https://atrium.lib.uoguelph.ca/xmlui/handle/10214/3863.

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

Council of Science Editors:

Morris K. Dimension Reduction for Model-based Clustering via Mixtures of Multivariate t-Distributions . [Thesis]. University of Guelph; 2012. Available from: https://atrium.lib.uoguelph.ca/xmlui/handle/10214/3863

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


Colorado State University

26. Emerson, Tegan Halley. A geometric data analysis approach to dimension reduction in machine learning and data mining in medical and biological sensing.

Degree: PhD, Mathematics, 2017, Colorado State University

 Geometric data analysis seeks to uncover and leverage structure in data for tasks in machine learning when data is visualized as points in some dimensional,… (more)

Subjects/Keywords: dimension reduction; Grassmannian manifold; data mining; machine learning; geometric data analysis

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APA (6th Edition):

Emerson, T. H. (2017). A geometric data analysis approach to dimension reduction in machine learning and data mining in medical and biological sensing. (Doctoral Dissertation). Colorado State University. Retrieved from http://hdl.handle.net/10217/183941

Chicago Manual of Style (16th Edition):

Emerson, Tegan Halley. “A geometric data analysis approach to dimension reduction in machine learning and data mining in medical and biological sensing.” 2017. Doctoral Dissertation, Colorado State University. Accessed February 20, 2018. http://hdl.handle.net/10217/183941.

MLA Handbook (7th Edition):

Emerson, Tegan Halley. “A geometric data analysis approach to dimension reduction in machine learning and data mining in medical and biological sensing.” 2017. Web. 20 Feb 2018.

Vancouver:

Emerson TH. A geometric data analysis approach to dimension reduction in machine learning and data mining in medical and biological sensing. [Internet] [Doctoral dissertation]. Colorado State University; 2017. [cited 2018 Feb 20]. Available from: http://hdl.handle.net/10217/183941.

Council of Science Editors:

Emerson TH. A geometric data analysis approach to dimension reduction in machine learning and data mining in medical and biological sensing. [Doctoral Dissertation]. Colorado State University; 2017. Available from: http://hdl.handle.net/10217/183941


Linnaeus University

27. Sun, Xuebo. An Application of Dimension Reduction for Intention Groups in Reddit.

Degree: Computer Science, 2016, Linnaeus University

  Reddit (www.reddit.com) is a social news platform for information sharing and exchanging. The amount of data, in terms of both observations and dimensions is… (more)

Subjects/Keywords: Reddit; communication model; dimension reduction; similarity metric; Computer Sciences; Datavetenskap (datalogi)

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

APA (6th Edition):

Sun, X. (2016). An Application of Dimension Reduction for Intention Groups in Reddit. (Thesis). Linnaeus University. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-56500

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, Xuebo. “An Application of Dimension Reduction for Intention Groups in Reddit.” 2016. Thesis, Linnaeus University. Accessed February 20, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-56500.

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

MLA Handbook (7th Edition):

Sun, Xuebo. “An Application of Dimension Reduction for Intention Groups in Reddit.” 2016. Web. 20 Feb 2018.

Vancouver:

Sun X. An Application of Dimension Reduction for Intention Groups in Reddit. [Internet] [Thesis]. Linnaeus University; 2016. [cited 2018 Feb 20]. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-56500.

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. An Application of Dimension Reduction for Intention Groups in Reddit. [Thesis]. Linnaeus University; 2016. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-56500

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


University of Cincinnati

28. Sun, Yan. Regularization for High-dimensional Time Series Models.

Degree: PhD, Arts and Sciences: Mathematical Sciences, 2011, University of Cincinnati

  Analyzing multivariate time series has been a very important topic in economics, finance, engineering, social and natural sciences. Compared to univariate models, the multivariate… (more)

Subjects/Keywords: Statistics; conditional likelihood; dimension reduction; oracle property; sparse; stationary; time series

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APA (6th Edition):

Sun, Y. (2011). Regularization for High-dimensional Time Series Models. (Doctoral Dissertation). University of Cincinnati. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=ucin1307321387

Chicago Manual of Style (16th Edition):

Sun, Yan. “Regularization for High-dimensional Time Series Models.” 2011. Doctoral Dissertation, University of Cincinnati. Accessed February 20, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1307321387.

MLA Handbook (7th Edition):

Sun, Yan. “Regularization for High-dimensional Time Series Models.” 2011. Web. 20 Feb 2018.

Vancouver:

Sun Y. Regularization for High-dimensional Time Series Models. [Internet] [Doctoral dissertation]. University of Cincinnati; 2011. [cited 2018 Feb 20]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ucin1307321387.

Council of Science Editors:

Sun Y. Regularization for High-dimensional Time Series Models. [Doctoral Dissertation]. University of Cincinnati; 2011. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ucin1307321387


University of Cincinnati

29. Zhou, Xuan. An Efficient Algorithm for Clustering Genomic Data.

Degree: MS, Engineering and Applied Science: Computer Science, 2014, University of Cincinnati

 In this thesis, we investigated an efficient framework for clustering analysis of gene expression profiles by discretizing continuous genomic data and adopting the 1D-jury approach… (more)

Subjects/Keywords: Computer Science; genomic data; clustering; discretization; 1D-Jury; dimension reduction

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APA (6th Edition):

Zhou, X. (2014). An Efficient Algorithm for Clustering Genomic Data. (Masters Thesis). University of Cincinnati. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=ucin1418910389

Chicago Manual of Style (16th Edition):

Zhou, Xuan. “An Efficient Algorithm for Clustering Genomic Data.” 2014. Masters Thesis, University of Cincinnati. Accessed February 20, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1418910389.

MLA Handbook (7th Edition):

Zhou, Xuan. “An Efficient Algorithm for Clustering Genomic Data.” 2014. Web. 20 Feb 2018.

Vancouver:

Zhou X. An Efficient Algorithm for Clustering Genomic Data. [Internet] [Masters thesis]. University of Cincinnati; 2014. [cited 2018 Feb 20]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ucin1418910389.

Council of Science Editors:

Zhou X. An Efficient Algorithm for Clustering Genomic Data. [Masters Thesis]. University of Cincinnati; 2014. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ucin1418910389


Duke University

30. Mao, Kai. Nonparametric Bayesian Models for Supervised Dimension Reduction and Regression.

Degree: 2009, Duke University

  We propose nonparametric Bayesian models for supervised dimension reduction and regression problems. Supervised dimension reduction is a setting where one needs to reduce the… (more)

Subjects/Keywords: Statistics; Dirichlet process; Kernel models; Nonparametric Bayesian; Supervised dimension reduction

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APA (6th Edition):

Mao, K. (2009). Nonparametric Bayesian Models for Supervised Dimension Reduction and Regression. (Thesis). Duke University. Retrieved from http://hdl.handle.net/10161/1581

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):

Mao, Kai. “Nonparametric Bayesian Models for Supervised Dimension Reduction and Regression.” 2009. Thesis, Duke University. Accessed February 20, 2018. http://hdl.handle.net/10161/1581.

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

MLA Handbook (7th Edition):

Mao, Kai. “Nonparametric Bayesian Models for Supervised Dimension Reduction and Regression.” 2009. Web. 20 Feb 2018.

Vancouver:

Mao K. Nonparametric Bayesian Models for Supervised Dimension Reduction and Regression. [Internet] [Thesis]. Duke University; 2009. [cited 2018 Feb 20]. Available from: http://hdl.handle.net/10161/1581.

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

Council of Science Editors:

Mao K. Nonparametric Bayesian Models for Supervised Dimension Reduction and Regression. [Thesis]. Duke University; 2009. Available from: http://hdl.handle.net/10161/1581

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

[1] [2] [3] [4] [5] [6]

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