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You searched for subject:(data reduction). Showing records 1 – 30 of 338 total matches.

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1. Dhungana, Prakash. Application for quick reduction of GPS data for urban mobility analysis.

Degree: 2014, Escola Superior Tecnologia e Gestão de Oliveira do Hospital

The potential requirement of using data reduction application as an assistive tool for the student and researchers has been designed, developed and deployed. For that,… (more)

Subjects/Keywords: Data analysis; Data reduction

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

APA (6th Edition):

Dhungana, P. (2014). Application for quick reduction of GPS data for urban mobility analysis. (Thesis). Escola Superior Tecnologia e Gestão de Oliveira do Hospital. Retrieved from https://www.rcaap.pt/detail.jsp?id=oai:comum.rcaap.pt:10400.26/17521

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):

Dhungana, Prakash. “Application for quick reduction of GPS data for urban mobility analysis.” 2014. Thesis, Escola Superior Tecnologia e Gestão de Oliveira do Hospital. Accessed February 26, 2021. https://www.rcaap.pt/detail.jsp?id=oai:comum.rcaap.pt:10400.26/17521.

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

MLA Handbook (7th Edition):

Dhungana, Prakash. “Application for quick reduction of GPS data for urban mobility analysis.” 2014. Web. 26 Feb 2021.

Vancouver:

Dhungana P. Application for quick reduction of GPS data for urban mobility analysis. [Internet] [Thesis]. Escola Superior Tecnologia e Gestão de Oliveira do Hospital; 2014. [cited 2021 Feb 26]. Available from: https://www.rcaap.pt/detail.jsp?id=oai:comum.rcaap.pt:10400.26/17521.

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

Council of Science Editors:

Dhungana P. Application for quick reduction of GPS data for urban mobility analysis. [Thesis]. Escola Superior Tecnologia e Gestão de Oliveira do Hospital; 2014. Available from: https://www.rcaap.pt/detail.jsp?id=oai:comum.rcaap.pt:10400.26/17521

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


Oregon State University

2. Kincaid, Thomas M. Estimating absence.

Degree: PhD, Statistics, 1997, Oregon State University

 The problem addressed is absence of a class of objects in a finite set of objects, which is investigated by considering absence of a species… (more)

Subjects/Keywords: Data reduction

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APA (6th Edition):

Kincaid, T. M. (1997). Estimating absence. (Doctoral Dissertation). Oregon State University. Retrieved from http://hdl.handle.net/1957/34074

Chicago Manual of Style (16th Edition):

Kincaid, Thomas M. “Estimating absence.” 1997. Doctoral Dissertation, Oregon State University. Accessed February 26, 2021. http://hdl.handle.net/1957/34074.

MLA Handbook (7th Edition):

Kincaid, Thomas M. “Estimating absence.” 1997. Web. 26 Feb 2021.

Vancouver:

Kincaid TM. Estimating absence. [Internet] [Doctoral dissertation]. Oregon State University; 1997. [cited 2021 Feb 26]. Available from: http://hdl.handle.net/1957/34074.

Council of Science Editors:

Kincaid TM. Estimating absence. [Doctoral Dissertation]. Oregon State University; 1997. Available from: http://hdl.handle.net/1957/34074


Clemson University

3. 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 26, 2021. https://tigerprints.clemson.edu/all_dissertations/1977.

MLA Handbook (7th Edition):

Knoll, Fiona. “Johnson-Lindenstrauss Transformations.” 2017. Web. 26 Feb 2021.

Vancouver:

Knoll F. Johnson-Lindenstrauss Transformations. [Internet] [Doctoral dissertation]. Clemson University; 2017. [cited 2021 Feb 26]. 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


NSYSU

4. Chen, Yu-rong. Effectively Aggregating Big Data for Visualization.

Degree: Master, Information Management, 2016, NSYSU

 With the fast development of the internet technologies, data is easily generated and collected. Those data could be useful based on how the enterprise or… (more)

Subjects/Keywords: Data Discretization; Exploratory Data Analysis; Big data; Data Reduction; Data Visualization

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APA (6th Edition):

Chen, Y. (2016). Effectively Aggregating Big Data for Visualization. (Thesis). NSYSU. Retrieved from http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0411116-090441

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, Yu-rong. “Effectively Aggregating Big Data for Visualization.” 2016. Thesis, NSYSU. Accessed February 26, 2021. http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0411116-090441.

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

MLA Handbook (7th Edition):

Chen, Yu-rong. “Effectively Aggregating Big Data for Visualization.” 2016. Web. 26 Feb 2021.

Vancouver:

Chen Y. Effectively Aggregating Big Data for Visualization. [Internet] [Thesis]. NSYSU; 2016. [cited 2021 Feb 26]. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0411116-090441.

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

Council of Science Editors:

Chen Y. Effectively Aggregating Big Data for Visualization. [Thesis]. NSYSU; 2016. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0411116-090441

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


University of Alberta

5. Fedoruk, John P. Dimensionality Reduction via the Johnson and Lindenstrauss Lemma: Mathematical and Computational Improvements.

Degree: MS, Department of Mathematical and Statistical Sciences, 2016, University of Alberta

 In an increasingly data-driven society, there is a growing need to simplify high-dimensional data sets. Over the course of the past three decades, the Johnson… (more)

Subjects/Keywords: Dimensionality Reduction; High Dimensional Data; Johnson Lindenstrauss

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APA (6th Edition):

Fedoruk, J. P. (2016). Dimensionality Reduction via the Johnson and Lindenstrauss Lemma: Mathematical and Computational Improvements. (Masters Thesis). University of Alberta. Retrieved from https://era.library.ualberta.ca/files/cm039k5065

Chicago Manual of Style (16th Edition):

Fedoruk, John P. “Dimensionality Reduction via the Johnson and Lindenstrauss Lemma: Mathematical and Computational Improvements.” 2016. Masters Thesis, University of Alberta. Accessed February 26, 2021. https://era.library.ualberta.ca/files/cm039k5065.

MLA Handbook (7th Edition):

Fedoruk, John P. “Dimensionality Reduction via the Johnson and Lindenstrauss Lemma: Mathematical and Computational Improvements.” 2016. Web. 26 Feb 2021.

Vancouver:

Fedoruk JP. Dimensionality Reduction via the Johnson and Lindenstrauss Lemma: Mathematical and Computational Improvements. [Internet] [Masters thesis]. University of Alberta; 2016. [cited 2021 Feb 26]. Available from: https://era.library.ualberta.ca/files/cm039k5065.

Council of Science Editors:

Fedoruk JP. Dimensionality Reduction via the Johnson and Lindenstrauss Lemma: Mathematical and Computational Improvements. [Masters Thesis]. University of Alberta; 2016. Available from: https://era.library.ualberta.ca/files/cm039k5065


Cornell University

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

Degree: PhD, Statistics, 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. (Doctoral Dissertation). Cornell University. Retrieved from http://hdl.handle.net/1813/37105

Chicago Manual of Style (16th Edition):

Chen, Maximillian. “Dimension Reduction And Inferential Procedures For Images.” 2014. Doctoral Dissertation, Cornell University. Accessed February 26, 2021. http://hdl.handle.net/1813/37105.

MLA Handbook (7th Edition):

Chen, Maximillian. “Dimension Reduction And Inferential Procedures For Images.” 2014. Web. 26 Feb 2021.

Vancouver:

Chen M. Dimension Reduction And Inferential Procedures For Images. [Internet] [Doctoral dissertation]. Cornell University; 2014. [cited 2021 Feb 26]. Available from: http://hdl.handle.net/1813/37105.

Council of Science Editors:

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

7. ALAKKARI, SALAHEDDIN. Modelling Large-scale Datasets Using Principal Component Analysis.

Degree: School of Computer Science & Statistics. Discipline of Computer Science, 2020, Trinity College Dublin

 Principal Component Analysis (PCA) is one of the most well-known unsupervised learning techniques used for dimensionality reduction and feature extraction. The main task of PCA… (more)

Subjects/Keywords: dimensionality reduction; unsupervised machine learning; data analysis

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APA (6th Edition):

ALAKKARI, S. (2020). Modelling Large-scale Datasets Using Principal Component Analysis. (Thesis). Trinity College Dublin. Retrieved from http://hdl.handle.net/2262/92525

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):

ALAKKARI, SALAHEDDIN. “Modelling Large-scale Datasets Using Principal Component Analysis.” 2020. Thesis, Trinity College Dublin. Accessed February 26, 2021. http://hdl.handle.net/2262/92525.

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

MLA Handbook (7th Edition):

ALAKKARI, SALAHEDDIN. “Modelling Large-scale Datasets Using Principal Component Analysis.” 2020. Web. 26 Feb 2021.

Vancouver:

ALAKKARI S. Modelling Large-scale Datasets Using Principal Component Analysis. [Internet] [Thesis]. Trinity College Dublin; 2020. [cited 2021 Feb 26]. Available from: http://hdl.handle.net/2262/92525.

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

Council of Science Editors:

ALAKKARI S. Modelling Large-scale Datasets Using Principal Component Analysis. [Thesis]. Trinity College Dublin; 2020. Available from: http://hdl.handle.net/2262/92525

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


Delft University of Technology

8. Krishnaraj, Manoj (author). Comparative Analysis of Techniques for Data Minimization for Recommender System algorithms.

Degree: 2019, Delft University of Technology

Recommender systems (RS) often use a large amount of data for a marginal gain in performance. This thesis investigates the data minimization in Recommender Systems,… (more)

Subjects/Keywords: Recommender Systems; Collaborative Filtering; Data Reduction

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APA (6th Edition):

Krishnaraj, M. (. (2019). Comparative Analysis of Techniques for Data Minimization for Recommender System algorithms. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:35e19f20-6161-4755-b9a5-7714af15a840

Chicago Manual of Style (16th Edition):

Krishnaraj, Manoj (author). “Comparative Analysis of Techniques for Data Minimization for Recommender System algorithms.” 2019. Masters Thesis, Delft University of Technology. Accessed February 26, 2021. http://resolver.tudelft.nl/uuid:35e19f20-6161-4755-b9a5-7714af15a840.

MLA Handbook (7th Edition):

Krishnaraj, Manoj (author). “Comparative Analysis of Techniques for Data Minimization for Recommender System algorithms.” 2019. Web. 26 Feb 2021.

Vancouver:

Krishnaraj M(. Comparative Analysis of Techniques for Data Minimization for Recommender System algorithms. [Internet] [Masters thesis]. Delft University of Technology; 2019. [cited 2021 Feb 26]. Available from: http://resolver.tudelft.nl/uuid:35e19f20-6161-4755-b9a5-7714af15a840.

Council of Science Editors:

Krishnaraj M(. Comparative Analysis of Techniques for Data Minimization for Recommender System algorithms. [Masters Thesis]. Delft University of Technology; 2019. Available from: http://resolver.tudelft.nl/uuid:35e19f20-6161-4755-b9a5-7714af15a840


University of Waterloo

9. Xie, Yijun. Applications of Projection Pursuit in Functional Data Analysis: Goodness-of- fit, Forecasting, and Change-point Detection.

Degree: 2021, University of Waterloo

 Dimension reduction methods for functional data have been avidly studied in recent years. However, existing methods are primarily based on summarizing the data by their… (more)

Subjects/Keywords: functional data analysis; dimension reduction; projection pursuit

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APA (6th Edition):

Xie, Y. (2021). Applications of Projection Pursuit in Functional Data Analysis: Goodness-of- fit, Forecasting, and Change-point Detection. (Thesis). University of Waterloo. Retrieved from http://hdl.handle.net/10012/16710

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):

Xie, Yijun. “Applications of Projection Pursuit in Functional Data Analysis: Goodness-of- fit, Forecasting, and Change-point Detection.” 2021. Thesis, University of Waterloo. Accessed February 26, 2021. http://hdl.handle.net/10012/16710.

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

MLA Handbook (7th Edition):

Xie, Yijun. “Applications of Projection Pursuit in Functional Data Analysis: Goodness-of- fit, Forecasting, and Change-point Detection.” 2021. Web. 26 Feb 2021.

Vancouver:

Xie Y. Applications of Projection Pursuit in Functional Data Analysis: Goodness-of- fit, Forecasting, and Change-point Detection. [Internet] [Thesis]. University of Waterloo; 2021. [cited 2021 Feb 26]. Available from: http://hdl.handle.net/10012/16710.

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

Council of Science Editors:

Xie Y. Applications of Projection Pursuit in Functional Data Analysis: Goodness-of- fit, Forecasting, and Change-point Detection. [Thesis]. University of Waterloo; 2021. Available from: http://hdl.handle.net/10012/16710

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


University of Ottawa

10. Baldiwala, Aliakbar. Dimensionality Reduction for Commercial Vehicle Fleet Monitoring .

Degree: 2018, University of Ottawa

 A variety of new features have been added in the present-day vehicles like a pre-crash warning, the vehicle to vehicle communication, semi-autonomous driving systems, telematics,… (more)

Subjects/Keywords: Data reduction; Fleet Monitoring system; CAN communication system; Comprehensive data reduction; Controller area network; CAN Busload reduction; Dimensionality reductioin

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APA (6th Edition):

Baldiwala, A. (2018). Dimensionality Reduction for Commercial Vehicle Fleet Monitoring . (Thesis). University of Ottawa. Retrieved from http://hdl.handle.net/10393/38330

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):

Baldiwala, Aliakbar. “Dimensionality Reduction for Commercial Vehicle Fleet Monitoring .” 2018. Thesis, University of Ottawa. Accessed February 26, 2021. http://hdl.handle.net/10393/38330.

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

MLA Handbook (7th Edition):

Baldiwala, Aliakbar. “Dimensionality Reduction for Commercial Vehicle Fleet Monitoring .” 2018. Web. 26 Feb 2021.

Vancouver:

Baldiwala A. Dimensionality Reduction for Commercial Vehicle Fleet Monitoring . [Internet] [Thesis]. University of Ottawa; 2018. [cited 2021 Feb 26]. Available from: http://hdl.handle.net/10393/38330.

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

Council of Science Editors:

Baldiwala A. Dimensionality Reduction for Commercial Vehicle Fleet Monitoring . [Thesis]. University of Ottawa; 2018. Available from: http://hdl.handle.net/10393/38330

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


University of Johannesburg

11. Immelman, Jaco. The effect of data reduction on LiDAR-based DEMs.

Degree: 2012, University of Johannesburg

M.Sc.

Light Detection and Ranging (LiDAR) provide decidedly accurate datasets with high data densities, in a very short time-span. However, the high volumes of data(more)

Subjects/Keywords: Digital elevation models; Remote sensing - Data processing; Optical radar; Data reduction

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APA (6th Edition):

Immelman, J. (2012). The effect of data reduction on LiDAR-based DEMs. (Thesis). University of Johannesburg. Retrieved from http://hdl.handle.net/10210/8064

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):

Immelman, Jaco. “The effect of data reduction on LiDAR-based DEMs.” 2012. Thesis, University of Johannesburg. Accessed February 26, 2021. http://hdl.handle.net/10210/8064.

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

MLA Handbook (7th Edition):

Immelman, Jaco. “The effect of data reduction on LiDAR-based DEMs.” 2012. Web. 26 Feb 2021.

Vancouver:

Immelman J. The effect of data reduction on LiDAR-based DEMs. [Internet] [Thesis]. University of Johannesburg; 2012. [cited 2021 Feb 26]. Available from: http://hdl.handle.net/10210/8064.

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

Council of Science Editors:

Immelman J. The effect of data reduction on LiDAR-based DEMs. [Thesis]. University of Johannesburg; 2012. Available from: http://hdl.handle.net/10210/8064

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


York University

12. Razavi, Nasim. Hierarchical Aggregate Structure by Inductive Aggregation for Interactive Data Visualization.

Degree: MSc -MS, Computer Science, 2017, York University

Data visualization is a method to facilitate the process of knowledge discovery and decision making. Effective and practical visual analytic systems have to support real-time… (more)

Subjects/Keywords: Computer science; Interactive data visualization; Data reduction; Database systems

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APA (6th Edition):

Razavi, N. (2017). Hierarchical Aggregate Structure by Inductive Aggregation for Interactive Data Visualization. (Masters Thesis). York University. Retrieved from http://hdl.handle.net/10315/33384

Chicago Manual of Style (16th Edition):

Razavi, Nasim. “Hierarchical Aggregate Structure by Inductive Aggregation for Interactive Data Visualization.” 2017. Masters Thesis, York University. Accessed February 26, 2021. http://hdl.handle.net/10315/33384.

MLA Handbook (7th Edition):

Razavi, Nasim. “Hierarchical Aggregate Structure by Inductive Aggregation for Interactive Data Visualization.” 2017. Web. 26 Feb 2021.

Vancouver:

Razavi N. Hierarchical Aggregate Structure by Inductive Aggregation for Interactive Data Visualization. [Internet] [Masters thesis]. York University; 2017. [cited 2021 Feb 26]. Available from: http://hdl.handle.net/10315/33384.

Council of Science Editors:

Razavi N. Hierarchical Aggregate Structure by Inductive Aggregation for Interactive Data Visualization. [Masters Thesis]. York University; 2017. Available from: http://hdl.handle.net/10315/33384

13. Bilgir Yetim, Ozlem. Minimizing Cellular Data Usage Using Application Delay Tolerance .

Degree: PhD, 2015, Princeton University

 Worldwide, mobile data usage has been increasing dramatically. As a result, users data usage costs increase. Mobile data offloading to WiFi where available could greatly… (more)

Subjects/Keywords: Cellular Data Usage Reduction; Data Offloading; Mobile Computing; WiFi Data Usage Improvement

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APA (6th Edition):

Bilgir Yetim, O. (2015). Minimizing Cellular Data Usage Using Application Delay Tolerance . (Doctoral Dissertation). Princeton University. Retrieved from http://arks.princeton.edu/ark:/88435/dsp014f16c505p

Chicago Manual of Style (16th Edition):

Bilgir Yetim, Ozlem. “Minimizing Cellular Data Usage Using Application Delay Tolerance .” 2015. Doctoral Dissertation, Princeton University. Accessed February 26, 2021. http://arks.princeton.edu/ark:/88435/dsp014f16c505p.

MLA Handbook (7th Edition):

Bilgir Yetim, Ozlem. “Minimizing Cellular Data Usage Using Application Delay Tolerance .” 2015. Web. 26 Feb 2021.

Vancouver:

Bilgir Yetim O. Minimizing Cellular Data Usage Using Application Delay Tolerance . [Internet] [Doctoral dissertation]. Princeton University; 2015. [cited 2021 Feb 26]. Available from: http://arks.princeton.edu/ark:/88435/dsp014f16c505p.

Council of Science Editors:

Bilgir Yetim O. Minimizing Cellular Data Usage Using Application Delay Tolerance . [Doctoral Dissertation]. Princeton University; 2015. Available from: http://arks.princeton.edu/ark:/88435/dsp014f16c505p


NSYSU

14. Ouyang, Jeng. A Similarity-based Data Reduction Approach.

Degree: Master, Electrical Engineering, 2009, NSYSU

 Finding an efficient data reduction method for large-scale problems is an imperative task. In this paper, we propose a similarity-based self-constructing fuzzy clustering algorithm to… (more)

Subjects/Keywords: fuzzy similarity; Large-scale dataset; data reduction; prototype reduction; instance-filtering; instance-abstraction

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APA (6th Edition):

Ouyang, J. (2009). A Similarity-based Data Reduction Approach. (Thesis). NSYSU. Retrieved from http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0907109-164128

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):

Ouyang, Jeng. “A Similarity-based Data Reduction Approach.” 2009. Thesis, NSYSU. Accessed February 26, 2021. http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0907109-164128.

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

MLA Handbook (7th Edition):

Ouyang, Jeng. “A Similarity-based Data Reduction Approach.” 2009. Web. 26 Feb 2021.

Vancouver:

Ouyang J. A Similarity-based Data Reduction Approach. [Internet] [Thesis]. NSYSU; 2009. [cited 2021 Feb 26]. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0907109-164128.

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

Council of Science Editors:

Ouyang J. A Similarity-based Data Reduction Approach. [Thesis]. NSYSU; 2009. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0907109-164128

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


Texas State University – San Marcos

15. Reiss, Randolf H. Eigenvalues and Eigenvectors in Data Dimension Reduction for Regression.

Degree: MS, Mathematics, 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 (6th Edition):

Reiss, R. H. (2013). Eigenvalues and Eigenvectors in Data Dimension Reduction for Regression. (Masters Thesis). Texas State University – San Marcos. Retrieved from https://digital.library.txstate.edu/handle/10877/4696

Chicago Manual of Style (16th Edition):

Reiss, Randolf H. “Eigenvalues and Eigenvectors in Data Dimension Reduction for Regression.” 2013. Masters Thesis, Texas State University – San Marcos. Accessed February 26, 2021. https://digital.library.txstate.edu/handle/10877/4696.

MLA Handbook (7th Edition):

Reiss, Randolf H. “Eigenvalues and Eigenvectors in Data Dimension Reduction for Regression.” 2013. Web. 26 Feb 2021.

Vancouver:

Reiss RH. Eigenvalues and Eigenvectors in Data Dimension Reduction for Regression. [Internet] [Masters thesis]. Texas State University – San Marcos; 2013. [cited 2021 Feb 26]. Available from: https://digital.library.txstate.edu/handle/10877/4696.

Council of Science Editors:

Reiss RH. Eigenvalues and Eigenvectors in Data Dimension Reduction for Regression. [Masters Thesis]. Texas State University – San Marcos; 2013. Available from: https://digital.library.txstate.edu/handle/10877/4696


RMIT University

16. Razavi Hesabi, Z. Mining complex data in highly streaming environments.

Degree: 2019, RMIT University

Data is growing at a rapid rate because of advanced hardware and software technologies and platforms such as e-health systems, sensor networks, and social media.… (more)

Subjects/Keywords: Fields of Research; data summarization; big data; machine learning; distributed computing; data management; dimensionality reduction; compression; clustering; histograms; index data structures

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APA (6th Edition):

Razavi Hesabi, Z. (2019). Mining complex data in highly streaming environments. (Thesis). RMIT University. Retrieved from http://researchbank.rmit.edu.au/view/rmit:162912

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):

Razavi Hesabi, Z. “Mining complex data in highly streaming environments.” 2019. Thesis, RMIT University. Accessed February 26, 2021. http://researchbank.rmit.edu.au/view/rmit:162912.

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

MLA Handbook (7th Edition):

Razavi Hesabi, Z. “Mining complex data in highly streaming environments.” 2019. Web. 26 Feb 2021.

Vancouver:

Razavi Hesabi Z. Mining complex data in highly streaming environments. [Internet] [Thesis]. RMIT University; 2019. [cited 2021 Feb 26]. Available from: http://researchbank.rmit.edu.au/view/rmit:162912.

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

Council of Science Editors:

Razavi Hesabi Z. Mining complex data in highly streaming environments. [Thesis]. RMIT University; 2019. Available from: http://researchbank.rmit.edu.au/view/rmit:162912

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


Penn State University

17. Laftchiev, Emil Ivanov. Robust Dynamical Model-based Data Representations and Structuring of Time Series Data for In-sequence Localization.

Degree: 2014, Penn State University

 In the modern era there is an unprecedented ability to actuate via an increasingly cheaper array of actuators, and to sense through a growing, increasingly… (more)

Subjects/Keywords: Data Dimension Reduction; Stochastic Programming; Multi-dimensional Data Representation; In-Sequence Localization

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APA (6th Edition):

Laftchiev, E. I. (2014). Robust Dynamical Model-based Data Representations and Structuring of Time Series Data for In-sequence Localization. (Thesis). Penn State University. Retrieved from https://submit-etda.libraries.psu.edu/catalog/23463

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):

Laftchiev, Emil Ivanov. “Robust Dynamical Model-based Data Representations and Structuring of Time Series Data for In-sequence Localization.” 2014. Thesis, Penn State University. Accessed February 26, 2021. https://submit-etda.libraries.psu.edu/catalog/23463.

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

MLA Handbook (7th Edition):

Laftchiev, Emil Ivanov. “Robust Dynamical Model-based Data Representations and Structuring of Time Series Data for In-sequence Localization.” 2014. Web. 26 Feb 2021.

Vancouver:

Laftchiev EI. Robust Dynamical Model-based Data Representations and Structuring of Time Series Data for In-sequence Localization. [Internet] [Thesis]. Penn State University; 2014. [cited 2021 Feb 26]. Available from: https://submit-etda.libraries.psu.edu/catalog/23463.

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

Council of Science Editors:

Laftchiev EI. Robust Dynamical Model-based Data Representations and Structuring of Time Series Data for In-sequence Localization. [Thesis]. Penn State University; 2014. Available from: https://submit-etda.libraries.psu.edu/catalog/23463

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


Penn State University

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

Degree: 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 (6th Edition):

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

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, Yang. “Approaches to reduce and integrate data in structured and high-dimensional regression problems in Genomics.” 2015. Thesis, Penn State University. Accessed February 26, 2021. https://submit-etda.libraries.psu.edu/catalog/26693.

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

MLA Handbook (7th Edition):

Liu, Yang. “Approaches to reduce and integrate data in structured and high-dimensional regression problems in Genomics.” 2015. Web. 26 Feb 2021.

Vancouver:

Liu Y. Approaches to reduce and integrate data in structured and high-dimensional regression problems in Genomics. [Internet] [Thesis]. Penn State University; 2015. [cited 2021 Feb 26]. Available from: https://submit-etda.libraries.psu.edu/catalog/26693.

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

Council of Science Editors:

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

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


George Mason University

19. Ashley, John M. Techniques for Exploring Cluster Compressed Geospatial-Temporal Satellite Datasets .

Degree: 2013, George Mason University

 NASA satellite data products are part of the recent big data explosion. An example of this are the individual physically referenced and processed footprints of… (more)

Subjects/Keywords: Computer science; Atmospheric sciences; Statistics; Climate Change; Data Compression; Data Reduction; Quantization; Remote Sensing; Visualization

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APA (6th Edition):

Ashley, J. M. (2013). Techniques for Exploring Cluster Compressed Geospatial-Temporal Satellite Datasets . (Thesis). George Mason University. Retrieved from http://hdl.handle.net/1920/8779

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):

Ashley, John M. “Techniques for Exploring Cluster Compressed Geospatial-Temporal Satellite Datasets .” 2013. Thesis, George Mason University. Accessed February 26, 2021. http://hdl.handle.net/1920/8779.

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

MLA Handbook (7th Edition):

Ashley, John M. “Techniques for Exploring Cluster Compressed Geospatial-Temporal Satellite Datasets .” 2013. Web. 26 Feb 2021.

Vancouver:

Ashley JM. Techniques for Exploring Cluster Compressed Geospatial-Temporal Satellite Datasets . [Internet] [Thesis]. George Mason University; 2013. [cited 2021 Feb 26]. Available from: http://hdl.handle.net/1920/8779.

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

Council of Science Editors:

Ashley JM. Techniques for Exploring Cluster Compressed Geospatial-Temporal Satellite Datasets . [Thesis]. George Mason University; 2013. Available from: http://hdl.handle.net/1920/8779

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


University of Melbourne

20. MONAZAM ERFANI, SARAH. Anomaly detection in participatory sensing networks.

Degree: 2015, University of Melbourne

 Anomaly detection or outlier detection aims to identify unusual values in a given dataset. In particular, there is growing interest in collaborative anomaly detection, where… (more)

Subjects/Keywords: anomaly detection; outlier detection; privacy-preserving data mining; big data; random projection; dimension reduction

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APA (6th Edition):

MONAZAM ERFANI, S. (2015). Anomaly detection in participatory sensing networks. (Doctoral Dissertation). University of Melbourne. Retrieved from http://hdl.handle.net/11343/58256

Chicago Manual of Style (16th Edition):

MONAZAM ERFANI, SARAH. “Anomaly detection in participatory sensing networks.” 2015. Doctoral Dissertation, University of Melbourne. Accessed February 26, 2021. http://hdl.handle.net/11343/58256.

MLA Handbook (7th Edition):

MONAZAM ERFANI, SARAH. “Anomaly detection in participatory sensing networks.” 2015. Web. 26 Feb 2021.

Vancouver:

MONAZAM ERFANI S. Anomaly detection in participatory sensing networks. [Internet] [Doctoral dissertation]. University of Melbourne; 2015. [cited 2021 Feb 26]. Available from: http://hdl.handle.net/11343/58256.

Council of Science Editors:

MONAZAM ERFANI S. Anomaly detection in participatory sensing networks. [Doctoral Dissertation]. University of Melbourne; 2015. Available from: http://hdl.handle.net/11343/58256


University of Canterbury

21. Henley, Lisa. The quantification and visualisation of human flourishing.

Degree: PhD, Mathematics, 2015, University of Canterbury

 Economic indicators such as GDP have been a main indicator of human progress since the first half of last century. There is concern that continuing… (more)

Subjects/Keywords: human flourishing; genetic algorithms; spectral clustering; data visualisation; data reduction; human progress

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APA (6th Edition):

Henley, L. (2015). The quantification and visualisation of human flourishing. (Doctoral Dissertation). University of Canterbury. Retrieved from http://dx.doi.org/10.26021/2506

Chicago Manual of Style (16th Edition):

Henley, Lisa. “The quantification and visualisation of human flourishing.” 2015. Doctoral Dissertation, University of Canterbury. Accessed February 26, 2021. http://dx.doi.org/10.26021/2506.

MLA Handbook (7th Edition):

Henley, Lisa. “The quantification and visualisation of human flourishing.” 2015. Web. 26 Feb 2021.

Vancouver:

Henley L. The quantification and visualisation of human flourishing. [Internet] [Doctoral dissertation]. University of Canterbury; 2015. [cited 2021 Feb 26]. Available from: http://dx.doi.org/10.26021/2506.

Council of Science Editors:

Henley L. The quantification and visualisation of human flourishing. [Doctoral Dissertation]. University of Canterbury; 2015. Available from: http://dx.doi.org/10.26021/2506

22. Landgraf, Andrew J. Generalized Principal Component Analysis: Dimensionality Reduction through the Projection of Natural Parameters.

Degree: PhD, Statistics, 2015, The Ohio State University

 Principal component analysis (PCA) is very useful for a wide variety of data analysis tasks, but its implicit connection to the Gaussian distribution can be… (more)

Subjects/Keywords: Statistics; Binary data; Count data; Dimensionality reduction; Exponential family; Logistic PCA; Principal component analysis

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

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APA (6th Edition):

Landgraf, A. J. (2015). Generalized Principal Component Analysis: Dimensionality Reduction through the Projection of Natural Parameters. (Doctoral Dissertation). The Ohio State University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=osu1437610558

Chicago Manual of Style (16th Edition):

Landgraf, Andrew J. “Generalized Principal Component Analysis: Dimensionality Reduction through the Projection of Natural Parameters.” 2015. Doctoral Dissertation, The Ohio State University. Accessed February 26, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=osu1437610558.

MLA Handbook (7th Edition):

Landgraf, Andrew J. “Generalized Principal Component Analysis: Dimensionality Reduction through the Projection of Natural Parameters.” 2015. Web. 26 Feb 2021.

Vancouver:

Landgraf AJ. Generalized Principal Component Analysis: Dimensionality Reduction through the Projection of Natural Parameters. [Internet] [Doctoral dissertation]. The Ohio State University; 2015. [cited 2021 Feb 26]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1437610558.

Council of Science Editors:

Landgraf AJ. Generalized Principal Component Analysis: Dimensionality Reduction through the Projection of Natural Parameters. [Doctoral Dissertation]. The Ohio State University; 2015. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1437610558


University of Newcastle

23. Shaw, Peter E. Advances in cluster editing: linear FPT kernels and comparative implementations.

Degree: PhD, 2010, University of Newcastle

Research Doctorate - Doctor of Philosophy (PhD)

Experience has shown that clustering objects into groups is a useful way to analyze and order information. It… (more)

Subjects/Keywords: combinatorial problems; data reduction; preprocessing; computational biology; fixed-parameter tractability

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APA (6th Edition):

Shaw, P. E. (2010). Advances in cluster editing: linear FPT kernels and comparative implementations. (Doctoral Dissertation). University of Newcastle. Retrieved from http://hdl.handle.net/1959.13/928253

Chicago Manual of Style (16th Edition):

Shaw, Peter E. “Advances in cluster editing: linear FPT kernels and comparative implementations.” 2010. Doctoral Dissertation, University of Newcastle. Accessed February 26, 2021. http://hdl.handle.net/1959.13/928253.

MLA Handbook (7th Edition):

Shaw, Peter E. “Advances in cluster editing: linear FPT kernels and comparative implementations.” 2010. Web. 26 Feb 2021.

Vancouver:

Shaw PE. Advances in cluster editing: linear FPT kernels and comparative implementations. [Internet] [Doctoral dissertation]. University of Newcastle; 2010. [cited 2021 Feb 26]. Available from: http://hdl.handle.net/1959.13/928253.

Council of Science Editors:

Shaw PE. Advances in cluster editing: linear FPT kernels and comparative implementations. [Doctoral Dissertation]. University of Newcastle; 2010. Available from: http://hdl.handle.net/1959.13/928253


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 https://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 26, 2021. https://scholarworks.rit.edu/theses/9324.

MLA Handbook (7th Edition):

Johnson, Juan Emmanuel. “Schroedinger Eigenmaps for Manifold Alignment of Multimodal Hyperspectral Images.” 2016. Web. 26 Feb 2021.

Vancouver:

Johnson JE. Schroedinger Eigenmaps for Manifold Alignment of Multimodal Hyperspectral Images. [Internet] [Masters thesis]. Rochester Institute of Technology; 2016. [cited 2021 Feb 26]. Available from: https://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: https://scholarworks.rit.edu/theses/9324


Vanderbilt University

25. Peterson, Emily Nancy. Assessment of Propensity Score Performance in Small Samples.

Degree: MS, Biostatistics, 2015, Vanderbilt University

 BIOSTATISTICS ASSESSMENT OF PROPENSITY SCORE PERFORMANCE IN SMALL SAMPLES EMILY PETERSON Thesis under the direction of Professor Tatsuki Koyama In observational studies, treatment selection is… (more)

Subjects/Keywords: propensity score models; small sample; simulation; data reduction

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APA (6th Edition):

Peterson, E. N. (2015). Assessment of Propensity Score Performance in Small Samples. (Thesis). Vanderbilt University. Retrieved from http://hdl.handle.net/1803/13205

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):

Peterson, Emily Nancy. “Assessment of Propensity Score Performance in Small Samples.” 2015. Thesis, Vanderbilt University. Accessed February 26, 2021. http://hdl.handle.net/1803/13205.

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

MLA Handbook (7th Edition):

Peterson, Emily Nancy. “Assessment of Propensity Score Performance in Small Samples.” 2015. Web. 26 Feb 2021.

Vancouver:

Peterson EN. Assessment of Propensity Score Performance in Small Samples. [Internet] [Thesis]. Vanderbilt University; 2015. [cited 2021 Feb 26]. Available from: http://hdl.handle.net/1803/13205.

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

Council of Science Editors:

Peterson EN. Assessment of Propensity Score Performance in Small Samples. [Thesis]. Vanderbilt University; 2015. Available from: http://hdl.handle.net/1803/13205

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


Stellenbosch University

26. Thomas, Jason Todd. The Use of Radar Data to Derive Areal Reduction Factors for South Africa.

Degree: MEng, Civil Engineering, 2019, Stellenbosch University

ENGLISH ABSTRACT: An areal reduction factor (ARF) can be defined as a factor that is applied to point rainfall depths to convert these depths to… (more)

Subjects/Keywords: Areal Reduction Factor; Hydrology; Radar  – Data; Storms; UCTD

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APA (6th Edition):

Thomas, J. T. (2019). The Use of Radar Data to Derive Areal Reduction Factors for South Africa. (Thesis). Stellenbosch University. Retrieved from http://hdl.handle.net/10019.1/107071

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):

Thomas, Jason Todd. “The Use of Radar Data to Derive Areal Reduction Factors for South Africa.” 2019. Thesis, Stellenbosch University. Accessed February 26, 2021. http://hdl.handle.net/10019.1/107071.

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

MLA Handbook (7th Edition):

Thomas, Jason Todd. “The Use of Radar Data to Derive Areal Reduction Factors for South Africa.” 2019. Web. 26 Feb 2021.

Vancouver:

Thomas JT. The Use of Radar Data to Derive Areal Reduction Factors for South Africa. [Internet] [Thesis]. Stellenbosch University; 2019. [cited 2021 Feb 26]. Available from: http://hdl.handle.net/10019.1/107071.

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

Council of Science Editors:

Thomas JT. The Use of Radar Data to Derive Areal Reduction Factors for South Africa. [Thesis]. Stellenbosch University; 2019. Available from: http://hdl.handle.net/10019.1/107071

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


Penn State University

27. Christou, Eliana. A Non-iterative Method for Fitting the Single Index Quantile Regression Model with Uncensored and Censored Data.

Degree: 2016, Penn State University

 Quantile regression (QR) is becoming increasingly popular due to its relevance in many scientific investigations. Linear and nonlinear QR models have been studied extensively, while… (more)

Subjects/Keywords: Censored Data; Dimension reduction; Index model; Nadaraya-Watson estimator; Quantile regression.

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APA (6th Edition):

Christou, E. (2016). A Non-iterative Method for Fitting the Single Index Quantile Regression Model with Uncensored and Censored Data. (Thesis). Penn State University. Retrieved from https://submit-etda.libraries.psu.edu/catalog/28762

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):

Christou, Eliana. “A Non-iterative Method for Fitting the Single Index Quantile Regression Model with Uncensored and Censored Data.” 2016. Thesis, Penn State University. Accessed February 26, 2021. https://submit-etda.libraries.psu.edu/catalog/28762.

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

MLA Handbook (7th Edition):

Christou, Eliana. “A Non-iterative Method for Fitting the Single Index Quantile Regression Model with Uncensored and Censored Data.” 2016. Web. 26 Feb 2021.

Vancouver:

Christou E. A Non-iterative Method for Fitting the Single Index Quantile Regression Model with Uncensored and Censored Data. [Internet] [Thesis]. Penn State University; 2016. [cited 2021 Feb 26]. Available from: https://submit-etda.libraries.psu.edu/catalog/28762.

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

Council of Science Editors:

Christou E. A Non-iterative Method for Fitting the Single Index Quantile Regression Model with Uncensored and Censored Data. [Thesis]. Penn State University; 2016. Available from: https://submit-etda.libraries.psu.edu/catalog/28762

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


University of Johannesburg

28. Visser, Hendrik Marthinus. The neural modelling of a direct reduction process.

Degree: 2014, University of Johannesburg

M.Ing. (Mechanical)

The goal of this study was to determine whether a SLIRN direct reduction process could be modelled with a neural network. The full… (more)

Subjects/Keywords: Direct reduction (Metallurgy) - Data processing; Iron - Metallurgy; Neural networks (Computer science)

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APA (6th Edition):

Visser, H. M. (2014). The neural modelling of a direct reduction process. (Thesis). University of Johannesburg. Retrieved from http://hdl.handle.net/10210/11773

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):

Visser, Hendrik Marthinus. “The neural modelling of a direct reduction process.” 2014. Thesis, University of Johannesburg. Accessed February 26, 2021. http://hdl.handle.net/10210/11773.

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

MLA Handbook (7th Edition):

Visser, Hendrik Marthinus. “The neural modelling of a direct reduction process.” 2014. Web. 26 Feb 2021.

Vancouver:

Visser HM. The neural modelling of a direct reduction process. [Internet] [Thesis]. University of Johannesburg; 2014. [cited 2021 Feb 26]. Available from: http://hdl.handle.net/10210/11773.

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

Council of Science Editors:

Visser HM. The neural modelling of a direct reduction process. [Thesis]. University of Johannesburg; 2014. Available from: http://hdl.handle.net/10210/11773

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

29. Munteanu, Alexander. On large-scale probabilistic and statistical data analysis.

Degree: 2018, Technische Universität Dortmund

 In this manuscript we develop and apply modern algorithmic data reduction techniques to tackle scalability issues and enable statistical data analysis of massive data sets.… (more)

Subjects/Keywords: Data reduction; Regression; Random projections; Coresets; 004; Datenkompression; Regressionsanalyse; Dimensionsreduktion

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APA (6th Edition):

Munteanu, A. (2018). On large-scale probabilistic and statistical data analysis. (Doctoral Dissertation). Technische Universität Dortmund. Retrieved from http://dx.doi.org/10.17877/DE290R-19112

Chicago Manual of Style (16th Edition):

Munteanu, Alexander. “On large-scale probabilistic and statistical data analysis.” 2018. Doctoral Dissertation, Technische Universität Dortmund. Accessed February 26, 2021. http://dx.doi.org/10.17877/DE290R-19112.

MLA Handbook (7th Edition):

Munteanu, Alexander. “On large-scale probabilistic and statistical data analysis.” 2018. Web. 26 Feb 2021.

Vancouver:

Munteanu A. On large-scale probabilistic and statistical data analysis. [Internet] [Doctoral dissertation]. Technische Universität Dortmund; 2018. [cited 2021 Feb 26]. Available from: http://dx.doi.org/10.17877/DE290R-19112.

Council of Science Editors:

Munteanu A. On large-scale probabilistic and statistical data analysis. [Doctoral Dissertation]. Technische Universität Dortmund; 2018. Available from: http://dx.doi.org/10.17877/DE290R-19112


University of Ontario Institute of Technology

30. Fernandez Espinosa, Arturo. Cluster techniques and prediction models for a digital media learning environment.

Degree: 2012, University of Ontario Institute of Technology

 The present work applies well-known data mining techniques in a digital learning media environment in order to identify groups of students based on their pro… (more)

Subjects/Keywords: Data mining; Neural networks; Custer techniques; Learning; Dimensionality reduction

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APA (6th Edition):

Fernandez Espinosa, A. (2012). Cluster techniques and prediction models for a digital media learning environment. (Thesis). University of Ontario Institute of Technology. Retrieved from http://hdl.handle.net/10155/241

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):

Fernandez Espinosa, Arturo. “Cluster techniques and prediction models for a digital media learning environment.” 2012. Thesis, University of Ontario Institute of Technology. Accessed February 26, 2021. http://hdl.handle.net/10155/241.

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

MLA Handbook (7th Edition):

Fernandez Espinosa, Arturo. “Cluster techniques and prediction models for a digital media learning environment.” 2012. Web. 26 Feb 2021.

Vancouver:

Fernandez Espinosa A. Cluster techniques and prediction models for a digital media learning environment. [Internet] [Thesis]. University of Ontario Institute of Technology; 2012. [cited 2021 Feb 26]. Available from: http://hdl.handle.net/10155/241.

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

Council of Science Editors:

Fernandez Espinosa A. Cluster techniques and prediction models for a digital media learning environment. [Thesis]. University of Ontario Institute of Technology; 2012. Available from: http://hdl.handle.net/10155/241

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

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