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You searched for subject:(data selection). Showing records 1 – 30 of 485 total matches.

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University of Southern California

1. Ren, Jie. Robust feature selection with penalized regression in imbalanced high dimensional data.

Degree: PhD, Statistical Genetics and Genetic Epidemiology, 2014, University of Southern California

 This work is motivated by an ongoing USC/Illumina study of prostate cancer recurrence after radical prostatectomy. The study generated gene expression data for nearly thirty… (more)

Subjects/Keywords: feature selection; penalized regression; imbalanced data; high dimensional data; stability selection

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

APA (6th Edition):

Ren, J. (2014). Robust feature selection with penalized regression in imbalanced high dimensional data. (Doctoral Dissertation). University of Southern California. Retrieved from http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/443080/rec/5613

Chicago Manual of Style (16th Edition):

Ren, Jie. “Robust feature selection with penalized regression in imbalanced high dimensional data.” 2014. Doctoral Dissertation, University of Southern California. Accessed February 18, 2020. http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/443080/rec/5613.

MLA Handbook (7th Edition):

Ren, Jie. “Robust feature selection with penalized regression in imbalanced high dimensional data.” 2014. Web. 18 Feb 2020.

Vancouver:

Ren J. Robust feature selection with penalized regression in imbalanced high dimensional data. [Internet] [Doctoral dissertation]. University of Southern California; 2014. [cited 2020 Feb 18]. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/443080/rec/5613.

Council of Science Editors:

Ren J. Robust feature selection with penalized regression in imbalanced high dimensional data. [Doctoral Dissertation]. University of Southern California; 2014. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/443080/rec/5613


University of Waterloo

2. Helwa, Ahmed Khairy Farahat. Greedy Representative Selection for Unsupervised Data Analysis.

Degree: 2013, University of Waterloo

 In recent years, the advance of information and communication technologies has allowed the storage and transfer of massive amounts of data. The availability of this… (more)

Subjects/Keywords: Data Mining; Machine Learning; Unsupervised Data Analysis; Greedy Algorithms; Representative Selection; Feature Selection; Data Clustering

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APA (6th Edition):

Helwa, A. K. F. (2013). Greedy Representative Selection for Unsupervised Data Analysis. (Thesis). University of Waterloo. Retrieved from http://hdl.handle.net/10012/7270

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):

Helwa, Ahmed Khairy Farahat. “Greedy Representative Selection for Unsupervised Data Analysis.” 2013. Thesis, University of Waterloo. Accessed February 18, 2020. http://hdl.handle.net/10012/7270.

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

MLA Handbook (7th Edition):

Helwa, Ahmed Khairy Farahat. “Greedy Representative Selection for Unsupervised Data Analysis.” 2013. Web. 18 Feb 2020.

Vancouver:

Helwa AKF. Greedy Representative Selection for Unsupervised Data Analysis. [Internet] [Thesis]. University of Waterloo; 2013. [cited 2020 Feb 18]. Available from: http://hdl.handle.net/10012/7270.

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

Council of Science Editors:

Helwa AKF. Greedy Representative Selection for Unsupervised Data Analysis. [Thesis]. University of Waterloo; 2013. Available from: http://hdl.handle.net/10012/7270

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


McMaster University

3. Armanfard, Narges. Localized Feature Selection for Classification.

Degree: PhD, 2017, McMaster University

The main idea of this thesis is to present the novel concept of localized feature selection (LFS) for data classification and its application for coma… (more)

Subjects/Keywords: Local Feature Selection; Data Classification; Coma Outcome Prediction; Feature Selection

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APA (6th Edition):

Armanfard, N. (2017). Localized Feature Selection for Classification. (Doctoral Dissertation). McMaster University. Retrieved from http://hdl.handle.net/11375/20944

Chicago Manual of Style (16th Edition):

Armanfard, Narges. “Localized Feature Selection for Classification.” 2017. Doctoral Dissertation, McMaster University. Accessed February 18, 2020. http://hdl.handle.net/11375/20944.

MLA Handbook (7th Edition):

Armanfard, Narges. “Localized Feature Selection for Classification.” 2017. Web. 18 Feb 2020.

Vancouver:

Armanfard N. Localized Feature Selection for Classification. [Internet] [Doctoral dissertation]. McMaster University; 2017. [cited 2020 Feb 18]. Available from: http://hdl.handle.net/11375/20944.

Council of Science Editors:

Armanfard N. Localized Feature Selection for Classification. [Doctoral Dissertation]. McMaster University; 2017. Available from: http://hdl.handle.net/11375/20944


Arizona State University

4. Zheng, Yi. An Information Based Optimal Subdata Selection Algorithm for Big Data Linear Regression and a Suitable Variable Selection Algorithm.

Degree: Statistics, 2017, Arizona State University

 This article proposes a new information-based subdata selection (IBOSS) algorithm, Squared Scaled Distance Algorithm (SSDA). It is based on the invariance of the determinant of… (more)

Subjects/Keywords: Statistics; Computer science; Big Data; IBOSS; Subdata Selection; Variable Selection

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APA (6th Edition):

Zheng, Y. (2017). An Information Based Optimal Subdata Selection Algorithm for Big Data Linear Regression and a Suitable Variable Selection Algorithm. (Masters Thesis). Arizona State University. Retrieved from http://repository.asu.edu/items/44253

Chicago Manual of Style (16th Edition):

Zheng, Yi. “An Information Based Optimal Subdata Selection Algorithm for Big Data Linear Regression and a Suitable Variable Selection Algorithm.” 2017. Masters Thesis, Arizona State University. Accessed February 18, 2020. http://repository.asu.edu/items/44253.

MLA Handbook (7th Edition):

Zheng, Yi. “An Information Based Optimal Subdata Selection Algorithm for Big Data Linear Regression and a Suitable Variable Selection Algorithm.” 2017. Web. 18 Feb 2020.

Vancouver:

Zheng Y. An Information Based Optimal Subdata Selection Algorithm for Big Data Linear Regression and a Suitable Variable Selection Algorithm. [Internet] [Masters thesis]. Arizona State University; 2017. [cited 2020 Feb 18]. Available from: http://repository.asu.edu/items/44253.

Council of Science Editors:

Zheng Y. An Information Based Optimal Subdata Selection Algorithm for Big Data Linear Regression and a Suitable Variable Selection Algorithm. [Masters Thesis]. Arizona State University; 2017. Available from: http://repository.asu.edu/items/44253


North Carolina State University

5. Shows, Justin Hall. Sparse Estimation and Inference for Censored Median Regression.

Degree: PhD, Statistics, 2009, North Carolina State University

 Censored median regression models have been shown to be useful for analyzing a variety of censored survival data with the robustness property. We study sparse… (more)

Subjects/Keywords: censored data; median regression; variable selection

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APA (6th Edition):

Shows, J. H. (2009). Sparse Estimation and Inference for Censored Median Regression. (Doctoral Dissertation). North Carolina State University. Retrieved from http://www.lib.ncsu.edu/resolver/1840.16/5565

Chicago Manual of Style (16th Edition):

Shows, Justin Hall. “Sparse Estimation and Inference for Censored Median Regression.” 2009. Doctoral Dissertation, North Carolina State University. Accessed February 18, 2020. http://www.lib.ncsu.edu/resolver/1840.16/5565.

MLA Handbook (7th Edition):

Shows, Justin Hall. “Sparse Estimation and Inference for Censored Median Regression.” 2009. Web. 18 Feb 2020.

Vancouver:

Shows JH. Sparse Estimation and Inference for Censored Median Regression. [Internet] [Doctoral dissertation]. North Carolina State University; 2009. [cited 2020 Feb 18]. Available from: http://www.lib.ncsu.edu/resolver/1840.16/5565.

Council of Science Editors:

Shows JH. Sparse Estimation and Inference for Censored Median Regression. [Doctoral Dissertation]. North Carolina State University; 2009. Available from: http://www.lib.ncsu.edu/resolver/1840.16/5565


University of Rochester

6. Pearson, Alexander T. Subset Selection for High-Dimensional Data, with Applications to Gene Array Data.

Degree: PhD, 2009, University of Rochester

 Identifying those genes that are differentially expressed in individuals with cancer could lead to new avenues of treatment or prevention. Gene array information can be… (more)

Subjects/Keywords: Subset Selection; Gene Array; High Dimensional Data

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APA (6th Edition):

Pearson, A. T. (2009). Subset Selection for High-Dimensional Data, with Applications to Gene Array Data. (Doctoral Dissertation). University of Rochester. Retrieved from http://hdl.handle.net/1802/8411

Chicago Manual of Style (16th Edition):

Pearson, Alexander T. “Subset Selection for High-Dimensional Data, with Applications to Gene Array Data.” 2009. Doctoral Dissertation, University of Rochester. Accessed February 18, 2020. http://hdl.handle.net/1802/8411.

MLA Handbook (7th Edition):

Pearson, Alexander T. “Subset Selection for High-Dimensional Data, with Applications to Gene Array Data.” 2009. Web. 18 Feb 2020.

Vancouver:

Pearson AT. Subset Selection for High-Dimensional Data, with Applications to Gene Array Data. [Internet] [Doctoral dissertation]. University of Rochester; 2009. [cited 2020 Feb 18]. Available from: http://hdl.handle.net/1802/8411.

Council of Science Editors:

Pearson AT. Subset Selection for High-Dimensional Data, with Applications to Gene Array Data. [Doctoral Dissertation]. University of Rochester; 2009. Available from: http://hdl.handle.net/1802/8411


University of Rochester

7. Evans, Katie N.; Love, Tanzy. Extensions to model-based clustering for mixed-type data : a new model framework, variable selection, and outlier detection.

Degree: PhD, 2014, University of Rochester

 In many disciplines, such as marketing, biology, and bioinformatics, there is an increasing desire to identify distinct subgroups of observations within an observed data set;… (more)

Subjects/Keywords: Mixed-type data; Feature selection; Outliers; Clustering

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APA (6th Edition):

Evans, Katie N.; Love, T. (2014). Extensions to model-based clustering for mixed-type data : a new model framework, variable selection, and outlier detection. (Doctoral Dissertation). University of Rochester. Retrieved from http://hdl.handle.net/1802/28443

Chicago Manual of Style (16th Edition):

Evans, Katie N.; Love, Tanzy. “Extensions to model-based clustering for mixed-type data : a new model framework, variable selection, and outlier detection.” 2014. Doctoral Dissertation, University of Rochester. Accessed February 18, 2020. http://hdl.handle.net/1802/28443.

MLA Handbook (7th Edition):

Evans, Katie N.; Love, Tanzy. “Extensions to model-based clustering for mixed-type data : a new model framework, variable selection, and outlier detection.” 2014. Web. 18 Feb 2020.

Vancouver:

Evans, Katie N.; Love T. Extensions to model-based clustering for mixed-type data : a new model framework, variable selection, and outlier detection. [Internet] [Doctoral dissertation]. University of Rochester; 2014. [cited 2020 Feb 18]. Available from: http://hdl.handle.net/1802/28443.

Council of Science Editors:

Evans, Katie N.; Love T. Extensions to model-based clustering for mixed-type data : a new model framework, variable selection, and outlier detection. [Doctoral Dissertation]. University of Rochester; 2014. Available from: http://hdl.handle.net/1802/28443


University of Cincinnati

8. Zhang, Yi. Application of Hyper-geometric Hypothesis-based Quantication and Markov Blanket Feature Selection Methods to Generate Signals for Adverse Drug Reaction Detection.

Degree: MS, Engineering and Applied Science: Mechanical Engineering, 2012, University of Cincinnati

 Pharmacovigilance is the science relating to all concerns about drug safety, especially ofmanaging the risk associated with medications. It serves as a complementary approach toclinical… (more)

Subjects/Keywords: Mechanical Engineering; Pharmacovigilance; Data Mining; Feature Selection

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APA (6th Edition):

Zhang, Y. (2012). Application of Hyper-geometric Hypothesis-based Quantication and Markov Blanket Feature Selection Methods to Generate Signals for Adverse Drug Reaction Detection. (Masters Thesis). University of Cincinnati. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=ucin1353343669

Chicago Manual of Style (16th Edition):

Zhang, Yi. “Application of Hyper-geometric Hypothesis-based Quantication and Markov Blanket Feature Selection Methods to Generate Signals for Adverse Drug Reaction Detection.” 2012. Masters Thesis, University of Cincinnati. Accessed February 18, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1353343669.

MLA Handbook (7th Edition):

Zhang, Yi. “Application of Hyper-geometric Hypothesis-based Quantication and Markov Blanket Feature Selection Methods to Generate Signals for Adverse Drug Reaction Detection.” 2012. Web. 18 Feb 2020.

Vancouver:

Zhang Y. Application of Hyper-geometric Hypothesis-based Quantication and Markov Blanket Feature Selection Methods to Generate Signals for Adverse Drug Reaction Detection. [Internet] [Masters thesis]. University of Cincinnati; 2012. [cited 2020 Feb 18]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ucin1353343669.

Council of Science Editors:

Zhang Y. Application of Hyper-geometric Hypothesis-based Quantication and Markov Blanket Feature Selection Methods to Generate Signals for Adverse Drug Reaction Detection. [Masters Thesis]. University of Cincinnati; 2012. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ucin1353343669


University of Cambridge

9. Ahfock, Daniel Christian. New statistical perspectives on efficient Big Data algorithms for high-dimensional Bayesian regression and model selection.

Degree: PhD, 2019, University of Cambridge

 This thesis is focused on the development of computationally efficient procedures for regression modelling with datasets containing a large number of observations. Standard algorithms be… (more)

Subjects/Keywords: Bayesian model selection; Random projection; Big Data

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APA (6th Edition):

Ahfock, D. C. (2019). New statistical perspectives on efficient Big Data algorithms for high-dimensional Bayesian regression and model selection. (Doctoral Dissertation). University of Cambridge. Retrieved from https://www.repository.cam.ac.uk/handle/1810/291805

Chicago Manual of Style (16th Edition):

Ahfock, Daniel Christian. “New statistical perspectives on efficient Big Data algorithms for high-dimensional Bayesian regression and model selection.” 2019. Doctoral Dissertation, University of Cambridge. Accessed February 18, 2020. https://www.repository.cam.ac.uk/handle/1810/291805.

MLA Handbook (7th Edition):

Ahfock, Daniel Christian. “New statistical perspectives on efficient Big Data algorithms for high-dimensional Bayesian regression and model selection.” 2019. Web. 18 Feb 2020.

Vancouver:

Ahfock DC. New statistical perspectives on efficient Big Data algorithms for high-dimensional Bayesian regression and model selection. [Internet] [Doctoral dissertation]. University of Cambridge; 2019. [cited 2020 Feb 18]. Available from: https://www.repository.cam.ac.uk/handle/1810/291805.

Council of Science Editors:

Ahfock DC. New statistical perspectives on efficient Big Data algorithms for high-dimensional Bayesian regression and model selection. [Doctoral Dissertation]. University of Cambridge; 2019. Available from: https://www.repository.cam.ac.uk/handle/1810/291805


Rice University

10. Peng, Yuhan. Static Cost Estimation for Data Layout Selection on GPUs.

Degree: MS, Computer Science, 2017, Rice University

 Performance modeling provides mathematical models and quantitative analysis for designing and optimizing computer systems and architectures. For many data-intensive applications, high-latency memory accesses often dominate… (more)

Subjects/Keywords: Data Layout Selection; Cost Estimation; GPUs

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APA (6th Edition):

Peng, Y. (2017). Static Cost Estimation for Data Layout Selection on GPUs. (Masters Thesis). Rice University. Retrieved from http://hdl.handle.net/1911/105451

Chicago Manual of Style (16th Edition):

Peng, Yuhan. “Static Cost Estimation for Data Layout Selection on GPUs.” 2017. Masters Thesis, Rice University. Accessed February 18, 2020. http://hdl.handle.net/1911/105451.

MLA Handbook (7th Edition):

Peng, Yuhan. “Static Cost Estimation for Data Layout Selection on GPUs.” 2017. Web. 18 Feb 2020.

Vancouver:

Peng Y. Static Cost Estimation for Data Layout Selection on GPUs. [Internet] [Masters thesis]. Rice University; 2017. [cited 2020 Feb 18]. Available from: http://hdl.handle.net/1911/105451.

Council of Science Editors:

Peng Y. Static Cost Estimation for Data Layout Selection on GPUs. [Masters Thesis]. Rice University; 2017. Available from: http://hdl.handle.net/1911/105451


University of Illinois – Chicago

11. Wei, Xiaokai. Unsupervised Feature Selection for Heterogeneous Data.

Degree: 2017, University of Illinois – Chicago

 In the era of big data, one is often confronted with the problem of high-dimensional data in many data mining applications. Hence, feature selection has… (more)

Subjects/Keywords: Feature Selection; Heterogeneous Data; Information Network

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APA (6th Edition):

Wei, X. (2017). Unsupervised Feature Selection for Heterogeneous Data. (Thesis). University of Illinois – Chicago. Retrieved from http://hdl.handle.net/10027/21855

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):

Wei, Xiaokai. “Unsupervised Feature Selection for Heterogeneous Data.” 2017. Thesis, University of Illinois – Chicago. Accessed February 18, 2020. http://hdl.handle.net/10027/21855.

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

MLA Handbook (7th Edition):

Wei, Xiaokai. “Unsupervised Feature Selection for Heterogeneous Data.” 2017. Web. 18 Feb 2020.

Vancouver:

Wei X. Unsupervised Feature Selection for Heterogeneous Data. [Internet] [Thesis]. University of Illinois – Chicago; 2017. [cited 2020 Feb 18]. Available from: http://hdl.handle.net/10027/21855.

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

Council of Science Editors:

Wei X. Unsupervised Feature Selection for Heterogeneous Data. [Thesis]. University of Illinois – Chicago; 2017. Available from: http://hdl.handle.net/10027/21855

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


Rice University

12. Peng, Yuhan. Static Cost Estimation for Data Layout Selection on GPUs.

Degree: MS, Engineering, 2017, Rice University

 Performance modeling provides mathematical models and quantitative analysis for designing and optimizing computer systems and architectures. For many data-intensive applications, high-latency memory accesses often dominate… (more)

Subjects/Keywords: Data Layout Selection; Cost Estimation; GPUs

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APA (6th Edition):

Peng, Y. (2017). Static Cost Estimation for Data Layout Selection on GPUs. (Masters Thesis). Rice University. Retrieved from http://hdl.handle.net/1911/105450

Chicago Manual of Style (16th Edition):

Peng, Yuhan. “Static Cost Estimation for Data Layout Selection on GPUs.” 2017. Masters Thesis, Rice University. Accessed February 18, 2020. http://hdl.handle.net/1911/105450.

MLA Handbook (7th Edition):

Peng, Yuhan. “Static Cost Estimation for Data Layout Selection on GPUs.” 2017. Web. 18 Feb 2020.

Vancouver:

Peng Y. Static Cost Estimation for Data Layout Selection on GPUs. [Internet] [Masters thesis]. Rice University; 2017. [cited 2020 Feb 18]. Available from: http://hdl.handle.net/1911/105450.

Council of Science Editors:

Peng Y. Static Cost Estimation for Data Layout Selection on GPUs. [Masters Thesis]. Rice University; 2017. Available from: http://hdl.handle.net/1911/105450


University of Technology, Sydney

13. Ubaudi, FA. Assessing a feature's trustworthiness and two approaches to feature selection.

Degree: 2011, University of Technology, Sydney

 Improvements in technology have led to a relentless deluge of information that current data mining approaches have trouble dealing with. An extreme example of this… (more)

Subjects/Keywords: Data mining.; Feature selection.; Feature trustworthiness.

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APA (6th Edition):

Ubaudi, F. (2011). Assessing a feature's trustworthiness and two approaches to feature selection. (Thesis). University of Technology, Sydney. Retrieved from http://hdl.handle.net/10453/23392

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):

Ubaudi, FA. “Assessing a feature's trustworthiness and two approaches to feature selection.” 2011. Thesis, University of Technology, Sydney. Accessed February 18, 2020. http://hdl.handle.net/10453/23392.

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

MLA Handbook (7th Edition):

Ubaudi, FA. “Assessing a feature's trustworthiness and two approaches to feature selection.” 2011. Web. 18 Feb 2020.

Vancouver:

Ubaudi F. Assessing a feature's trustworthiness and two approaches to feature selection. [Internet] [Thesis]. University of Technology, Sydney; 2011. [cited 2020 Feb 18]. Available from: http://hdl.handle.net/10453/23392.

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

Council of Science Editors:

Ubaudi F. Assessing a feature's trustworthiness and two approaches to feature selection. [Thesis]. University of Technology, Sydney; 2011. Available from: http://hdl.handle.net/10453/23392

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


University of Arizona

14. Fu, Tianjun. CSI in the Web 2.0 Age: Data Collection, Selection, and Investigation for Knowledge Discovery .

Degree: 2011, University of Arizona

 The growing popularity of various Web 2.0 media has created massive amounts of user-generated content such as online reviews, blog articles, shared videos, forums threads,… (more)

Subjects/Keywords: data mining; data selection; knowledge discovery; machine learning; Management Information Systems; data collection; data investigation

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APA (6th Edition):

Fu, T. (2011). CSI in the Web 2.0 Age: Data Collection, Selection, and Investigation for Knowledge Discovery . (Doctoral Dissertation). University of Arizona. Retrieved from http://hdl.handle.net/10150/217073

Chicago Manual of Style (16th Edition):

Fu, Tianjun. “CSI in the Web 2.0 Age: Data Collection, Selection, and Investigation for Knowledge Discovery .” 2011. Doctoral Dissertation, University of Arizona. Accessed February 18, 2020. http://hdl.handle.net/10150/217073.

MLA Handbook (7th Edition):

Fu, Tianjun. “CSI in the Web 2.0 Age: Data Collection, Selection, and Investigation for Knowledge Discovery .” 2011. Web. 18 Feb 2020.

Vancouver:

Fu T. CSI in the Web 2.0 Age: Data Collection, Selection, and Investigation for Knowledge Discovery . [Internet] [Doctoral dissertation]. University of Arizona; 2011. [cited 2020 Feb 18]. Available from: http://hdl.handle.net/10150/217073.

Council of Science Editors:

Fu T. CSI in the Web 2.0 Age: Data Collection, Selection, and Investigation for Knowledge Discovery . [Doctoral Dissertation]. University of Arizona; 2011. Available from: http://hdl.handle.net/10150/217073


Virginia Tech

15. Loftus, Stephen Christopher. On the Use of Grouped Covariate Regression in Oversaturated Models.

Degree: PhD, Statistics, 2015, Virginia Tech

 As data collection techniques improve, oftentimes the number of covariates exceeds the number of observations. When this happens, regression models become oversaturated and, thus, inestimable.… (more)

Subjects/Keywords: Oversaturated model; Big data; Variable selection; Data Analytics; Bayesian methods

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APA (6th Edition):

Loftus, S. C. (2015). On the Use of Grouped Covariate Regression in Oversaturated Models. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/64363

Chicago Manual of Style (16th Edition):

Loftus, Stephen Christopher. “On the Use of Grouped Covariate Regression in Oversaturated Models.” 2015. Doctoral Dissertation, Virginia Tech. Accessed February 18, 2020. http://hdl.handle.net/10919/64363.

MLA Handbook (7th Edition):

Loftus, Stephen Christopher. “On the Use of Grouped Covariate Regression in Oversaturated Models.” 2015. Web. 18 Feb 2020.

Vancouver:

Loftus SC. On the Use of Grouped Covariate Regression in Oversaturated Models. [Internet] [Doctoral dissertation]. Virginia Tech; 2015. [cited 2020 Feb 18]. Available from: http://hdl.handle.net/10919/64363.

Council of Science Editors:

Loftus SC. On the Use of Grouped Covariate Regression in Oversaturated Models. [Doctoral Dissertation]. Virginia Tech; 2015. Available from: http://hdl.handle.net/10919/64363


Delft University of Technology

16. Panagiotou, V. Blind segmentation of time-series: A two-level approach:.

Degree: 2015, Delft University of Technology

 Change-point detection is an indispensable tool for awide variety of applications which has been extensively studied in the literature over the years. However, the development… (more)

Subjects/Keywords: change-point detection; segmentation; time-series data; data selection techniques; speedup

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APA (6th Edition):

Panagiotou, V. (2015). Blind segmentation of time-series: A two-level approach:. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:832c8b73-fbc2-412e-9b8d-9063a48e6d57

Chicago Manual of Style (16th Edition):

Panagiotou, V. “Blind segmentation of time-series: A two-level approach:.” 2015. Masters Thesis, Delft University of Technology. Accessed February 18, 2020. http://resolver.tudelft.nl/uuid:832c8b73-fbc2-412e-9b8d-9063a48e6d57.

MLA Handbook (7th Edition):

Panagiotou, V. “Blind segmentation of time-series: A two-level approach:.” 2015. Web. 18 Feb 2020.

Vancouver:

Panagiotou V. Blind segmentation of time-series: A two-level approach:. [Internet] [Masters thesis]. Delft University of Technology; 2015. [cited 2020 Feb 18]. Available from: http://resolver.tudelft.nl/uuid:832c8b73-fbc2-412e-9b8d-9063a48e6d57.

Council of Science Editors:

Panagiotou V. Blind segmentation of time-series: A two-level approach:. [Masters Thesis]. Delft University of Technology; 2015. Available from: http://resolver.tudelft.nl/uuid:832c8b73-fbc2-412e-9b8d-9063a48e6d57


University of Illinois – Chicago

17. Liu, Cong. Investigation of Feature Selection Methods in High-Throughput Omics Data Analysis.

Degree: 2017, University of Illinois – Chicago

 High-throughput technology, such as microarray and next generation sequencing has accelerated the identification of uncovered biomarkers and developing of novel diagnosis approach in precision medicine.… (more)

Subjects/Keywords: feature selection; high-throughput omics data; data integration

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

APA (6th Edition):

Liu, C. (2017). Investigation of Feature Selection Methods in High-Throughput Omics Data Analysis. (Thesis). University of Illinois – Chicago. Retrieved from http://hdl.handle.net/10027/22136

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, Cong. “Investigation of Feature Selection Methods in High-Throughput Omics Data Analysis.” 2017. Thesis, University of Illinois – Chicago. Accessed February 18, 2020. http://hdl.handle.net/10027/22136.

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

MLA Handbook (7th Edition):

Liu, Cong. “Investigation of Feature Selection Methods in High-Throughput Omics Data Analysis.” 2017. Web. 18 Feb 2020.

Vancouver:

Liu C. Investigation of Feature Selection Methods in High-Throughput Omics Data Analysis. [Internet] [Thesis]. University of Illinois – Chicago; 2017. [cited 2020 Feb 18]. Available from: http://hdl.handle.net/10027/22136.

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

Council of Science Editors:

Liu C. Investigation of Feature Selection Methods in High-Throughput Omics Data Analysis. [Thesis]. University of Illinois – Chicago; 2017. Available from: http://hdl.handle.net/10027/22136

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


Temple University

18. Lou, Qiang. LEARNING FROM INCOMPLETE HIGH-DIMENSIONAL DATA.

Degree: PhD, 2013, Temple University

Computer and Information Science

Data sets with irrelevant and redundant features and large fraction of missing values are common in the real life application. Learning… (more)

Subjects/Keywords: Computer science; data mining; feature selection; high dimensional data; incomplete data; machine learning

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

APA (6th Edition):

Lou, Q. (2013). LEARNING FROM INCOMPLETE HIGH-DIMENSIONAL DATA. (Doctoral Dissertation). Temple University. Retrieved from http://digital.library.temple.edu/u?/p245801coll10,214785

Chicago Manual of Style (16th Edition):

Lou, Qiang. “LEARNING FROM INCOMPLETE HIGH-DIMENSIONAL DATA.” 2013. Doctoral Dissertation, Temple University. Accessed February 18, 2020. http://digital.library.temple.edu/u?/p245801coll10,214785.

MLA Handbook (7th Edition):

Lou, Qiang. “LEARNING FROM INCOMPLETE HIGH-DIMENSIONAL DATA.” 2013. Web. 18 Feb 2020.

Vancouver:

Lou Q. LEARNING FROM INCOMPLETE HIGH-DIMENSIONAL DATA. [Internet] [Doctoral dissertation]. Temple University; 2013. [cited 2020 Feb 18]. Available from: http://digital.library.temple.edu/u?/p245801coll10,214785.

Council of Science Editors:

Lou Q. LEARNING FROM INCOMPLETE HIGH-DIMENSIONAL DATA. [Doctoral Dissertation]. Temple University; 2013. Available from: http://digital.library.temple.edu/u?/p245801coll10,214785


Rice University

19. Sharma, Kamal Gopal. Locality Transformations of Computation and Data for Portable Performance.

Degree: PhD, Engineering, 2014, Rice University

 Recently, multi-cores chips have become omnipresent in computer systems ranging from high-end servers to mobile phones. A variety of multi-core architectures have been developed which… (more)

Subjects/Keywords: Locality Transformations; Cache Optimization; Tile size selection; Data Layout Optimization; Performance; Distributed Function Selection

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

APA (6th Edition):

Sharma, K. G. (2014). Locality Transformations of Computation and Data for Portable Performance. (Doctoral Dissertation). Rice University. Retrieved from http://hdl.handle.net/1911/88159

Chicago Manual of Style (16th Edition):

Sharma, Kamal Gopal. “Locality Transformations of Computation and Data for Portable Performance.” 2014. Doctoral Dissertation, Rice University. Accessed February 18, 2020. http://hdl.handle.net/1911/88159.

MLA Handbook (7th Edition):

Sharma, Kamal Gopal. “Locality Transformations of Computation and Data for Portable Performance.” 2014. Web. 18 Feb 2020.

Vancouver:

Sharma KG. Locality Transformations of Computation and Data for Portable Performance. [Internet] [Doctoral dissertation]. Rice University; 2014. [cited 2020 Feb 18]. Available from: http://hdl.handle.net/1911/88159.

Council of Science Editors:

Sharma KG. Locality Transformations of Computation and Data for Portable Performance. [Doctoral Dissertation]. Rice University; 2014. Available from: http://hdl.handle.net/1911/88159


University of Rochester

20. Singh, Kyra. Variable Selection Methods for Model-Based Clustering: Procedures for Functional Data and Bayesian Inference.

Degree: PhD, 2016, University of Rochester

Data is becoming more readily available and collected in larger and more frequent amounts as technology advances. Discrete, continuous, and time-course data are all easily… (more)

Subjects/Keywords: Clustering; Functional data; Mixture models; Model selection; Reversible-jump MCMC; Variance selection

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

APA (6th Edition):

Singh, K. (2016). Variable Selection Methods for Model-Based Clustering: Procedures for Functional Data and Bayesian Inference. (Doctoral Dissertation). University of Rochester. Retrieved from http://hdl.handle.net/1802/31625

Chicago Manual of Style (16th Edition):

Singh, Kyra. “Variable Selection Methods for Model-Based Clustering: Procedures for Functional Data and Bayesian Inference.” 2016. Doctoral Dissertation, University of Rochester. Accessed February 18, 2020. http://hdl.handle.net/1802/31625.

MLA Handbook (7th Edition):

Singh, Kyra. “Variable Selection Methods for Model-Based Clustering: Procedures for Functional Data and Bayesian Inference.” 2016. Web. 18 Feb 2020.

Vancouver:

Singh K. Variable Selection Methods for Model-Based Clustering: Procedures for Functional Data and Bayesian Inference. [Internet] [Doctoral dissertation]. University of Rochester; 2016. [cited 2020 Feb 18]. Available from: http://hdl.handle.net/1802/31625.

Council of Science Editors:

Singh K. Variable Selection Methods for Model-Based Clustering: Procedures for Functional Data and Bayesian Inference. [Doctoral Dissertation]. University of Rochester; 2016. Available from: http://hdl.handle.net/1802/31625


Arizona State University

21. Davila, Saylisse. Public Health Surveillance in High-Dimensions with Supervised Learning.

Degree: PhD, Industrial Engineering, 2010, Arizona State University

 Public health surveillance is a special case of the general problem where counts (or rates) of events are monitored for changes. Modern data complements event… (more)

Subjects/Keywords: Industrial Engineering; Public Health; Statistics; Data Mining; Feature Selection; Feature Value Selection; Public Health Surveillance

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

APA (6th Edition):

Davila, S. (2010). Public Health Surveillance in High-Dimensions with Supervised Learning. (Doctoral Dissertation). Arizona State University. Retrieved from http://repository.asu.edu/items/8738

Chicago Manual of Style (16th Edition):

Davila, Saylisse. “Public Health Surveillance in High-Dimensions with Supervised Learning.” 2010. Doctoral Dissertation, Arizona State University. Accessed February 18, 2020. http://repository.asu.edu/items/8738.

MLA Handbook (7th Edition):

Davila, Saylisse. “Public Health Surveillance in High-Dimensions with Supervised Learning.” 2010. Web. 18 Feb 2020.

Vancouver:

Davila S. Public Health Surveillance in High-Dimensions with Supervised Learning. [Internet] [Doctoral dissertation]. Arizona State University; 2010. [cited 2020 Feb 18]. Available from: http://repository.asu.edu/items/8738.

Council of Science Editors:

Davila S. Public Health Surveillance in High-Dimensions with Supervised Learning. [Doctoral Dissertation]. Arizona State University; 2010. Available from: http://repository.asu.edu/items/8738


KTH

22. Anette, Kniberg. A Benchmark of Prevalent Feature Selection Algorithms on a Diverse Set of Classification Problems.

Degree: Biomedical Engineering and Health Systems, 2018, KTH

Feature selection is the process of automatically selecting important features from data. It is an essential part of machine learning, artificial intelligence, data mining,… (more)

Subjects/Keywords: feature selection; variable selection; attribute selection; machine learning; data mining; benchmark; classification; variabelselektion; maskininlärning; datautvinning; klassificering; Medical Engineering; Medicinteknik

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

APA (6th Edition):

Anette, K. (2018). A Benchmark of Prevalent Feature Selection Algorithms on a Diverse Set of Classification Problems. (Thesis). KTH. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-228614

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):

Anette, Kniberg. “A Benchmark of Prevalent Feature Selection Algorithms on a Diverse Set of Classification Problems.” 2018. Thesis, KTH. Accessed February 18, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-228614.

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

MLA Handbook (7th Edition):

Anette, Kniberg. “A Benchmark of Prevalent Feature Selection Algorithms on a Diverse Set of Classification Problems.” 2018. Web. 18 Feb 2020.

Vancouver:

Anette K. A Benchmark of Prevalent Feature Selection Algorithms on a Diverse Set of Classification Problems. [Internet] [Thesis]. KTH; 2018. [cited 2020 Feb 18]. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-228614.

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

Council of Science Editors:

Anette K. A Benchmark of Prevalent Feature Selection Algorithms on a Diverse Set of Classification Problems. [Thesis]. KTH; 2018. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-228614

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


University of Manchester

23. Cortes Rios, Julio Cesar. Targeted Feedback Collection for Data Source Selection with Uncertainty.

Degree: 2018, University of Manchester

 The aim of this dissertation is to contribute to research on pay-as-you-go data integration through the proposal of an approach for targeted feedback collection (TFC),… (more)

Subjects/Keywords: Data integration; Data source selection; Schema mapping selection; Feedback collection; Pay-as-you-go; Optimisation; Crowd-sourcing; Uncertainty handling

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

APA (6th Edition):

Cortes Rios, J. C. (2018). Targeted Feedback Collection for Data Source Selection with Uncertainty. (Doctoral Dissertation). University of Manchester. Retrieved from http://www.manchester.ac.uk/escholar/uk-ac-man-scw:314106

Chicago Manual of Style (16th Edition):

Cortes Rios, Julio Cesar. “Targeted Feedback Collection for Data Source Selection with Uncertainty.” 2018. Doctoral Dissertation, University of Manchester. Accessed February 18, 2020. http://www.manchester.ac.uk/escholar/uk-ac-man-scw:314106.

MLA Handbook (7th Edition):

Cortes Rios, Julio Cesar. “Targeted Feedback Collection for Data Source Selection with Uncertainty.” 2018. Web. 18 Feb 2020.

Vancouver:

Cortes Rios JC. Targeted Feedback Collection for Data Source Selection with Uncertainty. [Internet] [Doctoral dissertation]. University of Manchester; 2018. [cited 2020 Feb 18]. Available from: http://www.manchester.ac.uk/escholar/uk-ac-man-scw:314106.

Council of Science Editors:

Cortes Rios JC. Targeted Feedback Collection for Data Source Selection with Uncertainty. [Doctoral Dissertation]. University of Manchester; 2018. Available from: http://www.manchester.ac.uk/escholar/uk-ac-man-scw:314106


University of Pretoria

24. Van der Walt, Christiaan Maarten. Data measures that characterise classification problems.

Degree: Electrical, Electronic and Computer Engineering, 2008, University of Pretoria

 We have a wide-range of classifiers today that are employed in numerous applications, from credit scoring to speech-processing, with great technical and commercial success. No… (more)

Subjects/Keywords: Classifier selection; Data measures; Data characteristics; Artificial data; Data analysis; Classification; Supervised learning; Pattern recognition; Meta-classification; Classification prediction; UCTD

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

APA (6th Edition):

Van der Walt, C. (2008). Data measures that characterise classification problems. (Masters Thesis). University of Pretoria. Retrieved from http://hdl.handle.net/2263/27624

Chicago Manual of Style (16th Edition):

Van der Walt, Christiaan. “Data measures that characterise classification problems.” 2008. Masters Thesis, University of Pretoria. Accessed February 18, 2020. http://hdl.handle.net/2263/27624.

MLA Handbook (7th Edition):

Van der Walt, Christiaan. “Data measures that characterise classification problems.” 2008. Web. 18 Feb 2020.

Vancouver:

Van der Walt C. Data measures that characterise classification problems. [Internet] [Masters thesis]. University of Pretoria; 2008. [cited 2020 Feb 18]. Available from: http://hdl.handle.net/2263/27624.

Council of Science Editors:

Van der Walt C. Data measures that characterise classification problems. [Masters Thesis]. University of Pretoria; 2008. Available from: http://hdl.handle.net/2263/27624


University of Pretoria

25. [No author]. Data measures that characterise classification problems .

Degree: 2008, University of Pretoria

 We have a wide-range of classifiers today that are employed in numerous applications, from credit scoring to speech-processing, with great technical and commercial success. No… (more)

Subjects/Keywords: Classifier selection; Data measures; Data characteristics; Artificial data; Data analysis; Classification; Supervised learning; Pattern recognition; Meta-classification; Classification prediction; UCTD

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

APA (6th Edition):

author], [. (2008). Data measures that characterise classification problems . (Masters Thesis). University of Pretoria. Retrieved from http://upetd.up.ac.za/thesis/available/etd-08292008-162648/

Chicago Manual of Style (16th Edition):

author], [No. “Data measures that characterise classification problems .” 2008. Masters Thesis, University of Pretoria. Accessed February 18, 2020. http://upetd.up.ac.za/thesis/available/etd-08292008-162648/.

MLA Handbook (7th Edition):

author], [No. “Data measures that characterise classification problems .” 2008. Web. 18 Feb 2020.

Vancouver:

author] [. Data measures that characterise classification problems . [Internet] [Masters thesis]. University of Pretoria; 2008. [cited 2020 Feb 18]. Available from: http://upetd.up.ac.za/thesis/available/etd-08292008-162648/.

Council of Science Editors:

author] [. Data measures that characterise classification problems . [Masters Thesis]. University of Pretoria; 2008. Available from: http://upetd.up.ac.za/thesis/available/etd-08292008-162648/


University of Illinois – Chicago

26. Kong, Xiangnan. Modeling Big Data Variety with Graph Mining Techniques.

Degree: 2014, University of Illinois – Chicago

 Graphs are ubiquitous and have become increasingly important in modeling diverse kinds of objects. In many real-world applications, instances are not represented as feature vectors,… (more)

Subjects/Keywords: Graph Mining; Data Mining; Big Data; Data Variety; Subgraph Pattern; Feature Selection; Uncertain Data; Drug Discovery; Brain Network

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

APA (6th Edition):

Kong, X. (2014). Modeling Big Data Variety with Graph Mining Techniques. (Thesis). University of Illinois – Chicago. Retrieved from http://hdl.handle.net/10027/19119

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):

Kong, Xiangnan. “Modeling Big Data Variety with Graph Mining Techniques.” 2014. Thesis, University of Illinois – Chicago. Accessed February 18, 2020. http://hdl.handle.net/10027/19119.

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

MLA Handbook (7th Edition):

Kong, Xiangnan. “Modeling Big Data Variety with Graph Mining Techniques.” 2014. Web. 18 Feb 2020.

Vancouver:

Kong X. Modeling Big Data Variety with Graph Mining Techniques. [Internet] [Thesis]. University of Illinois – Chicago; 2014. [cited 2020 Feb 18]. Available from: http://hdl.handle.net/10027/19119.

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

Council of Science Editors:

Kong X. Modeling Big Data Variety with Graph Mining Techniques. [Thesis]. University of Illinois – Chicago; 2014. Available from: http://hdl.handle.net/10027/19119

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


Rice University

27. Wadsworth, W Duncan. Bayesian Methods for the Analysis of Microbiome Data.

Degree: PhD, Engineering, 2016, Rice University

 Bacteria, archaea, viruses, and fungi are present in large numbers both on and inside of our bodies. On average, only one in ten of “our”… (more)

Subjects/Keywords: Bayesian hierarchical model; Data integration; Dirichlet-Multinomial; Microbiome data; Variable selection; Multiple testing; Bayesian nonparametrics

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

APA (6th Edition):

Wadsworth, W. D. (2016). Bayesian Methods for the Analysis of Microbiome Data. (Doctoral Dissertation). Rice University. Retrieved from http://hdl.handle.net/1911/95977

Chicago Manual of Style (16th Edition):

Wadsworth, W Duncan. “Bayesian Methods for the Analysis of Microbiome Data.” 2016. Doctoral Dissertation, Rice University. Accessed February 18, 2020. http://hdl.handle.net/1911/95977.

MLA Handbook (7th Edition):

Wadsworth, W Duncan. “Bayesian Methods for the Analysis of Microbiome Data.” 2016. Web. 18 Feb 2020.

Vancouver:

Wadsworth WD. Bayesian Methods for the Analysis of Microbiome Data. [Internet] [Doctoral dissertation]. Rice University; 2016. [cited 2020 Feb 18]. Available from: http://hdl.handle.net/1911/95977.

Council of Science Editors:

Wadsworth WD. Bayesian Methods for the Analysis of Microbiome Data. [Doctoral Dissertation]. Rice University; 2016. Available from: http://hdl.handle.net/1911/95977


Penn State University

28. Liu, Yang. Approaches to reduce and integrate data in structured and high-dimensional regression problems in Genomics.

Degree: PhD, Statistics, 2015, Penn State University

 Analysis of high-dimensional data has become increasingly important in several fields of the sciences and engineering. This is particularly true for Genomics with its expanding… (more)

Subjects/Keywords: Data integration; Genomics; Ordinary least squares; Structured data; Sufficient dimension reduction; Variable selection

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

APA (6th Edition):

Liu, Y. (2015). Approaches to reduce and integrate data in structured and high-dimensional regression problems in Genomics. (Doctoral Dissertation). Penn State University. Retrieved from https://etda.libraries.psu.edu/catalog/26693

Chicago Manual of Style (16th Edition):

Liu, Yang. “Approaches to reduce and integrate data in structured and high-dimensional regression problems in Genomics.” 2015. Doctoral Dissertation, Penn State University. Accessed February 18, 2020. https://etda.libraries.psu.edu/catalog/26693.

MLA Handbook (7th Edition):

Liu, Yang. “Approaches to reduce and integrate data in structured and high-dimensional regression problems in Genomics.” 2015. Web. 18 Feb 2020.

Vancouver:

Liu Y. Approaches to reduce and integrate data in structured and high-dimensional regression problems in Genomics. [Internet] [Doctoral dissertation]. Penn State University; 2015. [cited 2020 Feb 18]. Available from: https://etda.libraries.psu.edu/catalog/26693.

Council of Science Editors:

Liu Y. Approaches to reduce and integrate data in structured and high-dimensional regression problems in Genomics. [Doctoral Dissertation]. Penn State University; 2015. Available from: https://etda.libraries.psu.edu/catalog/26693


IUPUI

29. Koka, Keerthika. Feature selection through visualisation for the classification of online reviews.

Degree: 2017, IUPUI

Indiana University-Purdue University Indianapolis (IUPUI)

The purpose of this work is to prove that the visualization is at least as powerful as the best automatic… (more)

Subjects/Keywords: Text Visual analytics; Data visualisation; Online reviews classification; Multi-dimensional data visualisation; Visual feature selection

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

APA (6th Edition):

Koka, K. (2017). Feature selection through visualisation for the classification of online reviews. (Thesis). IUPUI. Retrieved from http://hdl.handle.net/1805/12483

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):

Koka, Keerthika. “Feature selection through visualisation for the classification of online reviews.” 2017. Thesis, IUPUI. Accessed February 18, 2020. http://hdl.handle.net/1805/12483.

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

MLA Handbook (7th Edition):

Koka, Keerthika. “Feature selection through visualisation for the classification of online reviews.” 2017. Web. 18 Feb 2020.

Vancouver:

Koka K. Feature selection through visualisation for the classification of online reviews. [Internet] [Thesis]. IUPUI; 2017. [cited 2020 Feb 18]. Available from: http://hdl.handle.net/1805/12483.

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

Council of Science Editors:

Koka K. Feature selection through visualisation for the classification of online reviews. [Thesis]. IUPUI; 2017. Available from: http://hdl.handle.net/1805/12483

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


University of Sheffield

30. Hopkins, Julie Anne. Sampling designs for exploratory multivariate analysis.

Degree: PhD, 2000, University of Sheffield

 This thesis is concerned with problems of variable selection, influence of sample size and related issues in the applications of various techniques of exploratory multivariate… (more)

Subjects/Keywords: 519.5; Variable selection; Data sets

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

APA (6th Edition):

Hopkins, J. A. (2000). Sampling designs for exploratory multivariate analysis. (Doctoral Dissertation). University of Sheffield. Retrieved from http://etheses.whiterose.ac.uk/14798/ ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.323197

Chicago Manual of Style (16th Edition):

Hopkins, Julie Anne. “Sampling designs for exploratory multivariate analysis.” 2000. Doctoral Dissertation, University of Sheffield. Accessed February 18, 2020. http://etheses.whiterose.ac.uk/14798/ ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.323197.

MLA Handbook (7th Edition):

Hopkins, Julie Anne. “Sampling designs for exploratory multivariate analysis.” 2000. Web. 18 Feb 2020.

Vancouver:

Hopkins JA. Sampling designs for exploratory multivariate analysis. [Internet] [Doctoral dissertation]. University of Sheffield; 2000. [cited 2020 Feb 18]. Available from: http://etheses.whiterose.ac.uk/14798/ ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.323197.

Council of Science Editors:

Hopkins JA. Sampling designs for exploratory multivariate analysis. [Doctoral Dissertation]. University of Sheffield; 2000. Available from: http://etheses.whiterose.ac.uk/14798/ ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.323197

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