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You searched for subject:(Feature learning). Showing records 1 – 30 of 404 total matches.

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University of Otago

1. Fu, Jie. Learning Hierarchical Sparse Filters for Feature Matching .

Degree: 2012, University of Otago

 A common problem in computer vision is to match corresponding points between images. The success of computer vision has usually relied on having good feature(more)

Subjects/Keywords: Feature Learning; Feature Matching; Computer Vision

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APA (6th Edition):

Fu, J. (2012). Learning Hierarchical Sparse Filters for Feature Matching . (Masters Thesis). University of Otago. Retrieved from http://hdl.handle.net/10523/2360

Chicago Manual of Style (16th Edition):

Fu, Jie. “Learning Hierarchical Sparse Filters for Feature Matching .” 2012. Masters Thesis, University of Otago. Accessed March 18, 2019. http://hdl.handle.net/10523/2360.

MLA Handbook (7th Edition):

Fu, Jie. “Learning Hierarchical Sparse Filters for Feature Matching .” 2012. Web. 18 Mar 2019.

Vancouver:

Fu J. Learning Hierarchical Sparse Filters for Feature Matching . [Internet] [Masters thesis]. University of Otago; 2012. [cited 2019 Mar 18]. Available from: http://hdl.handle.net/10523/2360.

Council of Science Editors:

Fu J. Learning Hierarchical Sparse Filters for Feature Matching . [Masters Thesis]. University of Otago; 2012. Available from: http://hdl.handle.net/10523/2360


University of Alberta

2. KIRCI, MESUT. Feature learning using state differences.

Degree: MS, Department of Computing Science, 2010, University of Alberta

 Domain-independent feature learning is a hard problem. This is reflected by lack of broad research in the area. The goal of General Game Playing (GGP)… (more)

Subjects/Keywords: general game playing; feature learning

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APA (6th Edition):

KIRCI, M. (2010). Feature learning using state differences. (Masters Thesis). University of Alberta. Retrieved from https://era.library.ualberta.ca/files/vt150k34n

Chicago Manual of Style (16th Edition):

KIRCI, MESUT. “Feature learning using state differences.” 2010. Masters Thesis, University of Alberta. Accessed March 18, 2019. https://era.library.ualberta.ca/files/vt150k34n.

MLA Handbook (7th Edition):

KIRCI, MESUT. “Feature learning using state differences.” 2010. Web. 18 Mar 2019.

Vancouver:

KIRCI M. Feature learning using state differences. [Internet] [Masters thesis]. University of Alberta; 2010. [cited 2019 Mar 18]. Available from: https://era.library.ualberta.ca/files/vt150k34n.

Council of Science Editors:

KIRCI M. Feature learning using state differences. [Masters Thesis]. University of Alberta; 2010. Available from: https://era.library.ualberta.ca/files/vt150k34n


University of Illinois – Chicago

3. Huang, Yi. Problems in Learning under Limited Resources and Information.

Degree: 2017, University of Illinois – Chicago

 The main theme of this thesis is to investigate how learning problems can be solved in the face of limited resources and with limited information… (more)

Subjects/Keywords: feature-efficient learning; network construction

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APA (6th Edition):

Huang, Y. (2017). Problems in Learning under Limited Resources and Information. (Thesis). University of Illinois – Chicago. Retrieved from http://hdl.handle.net/10027/21988

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):

Huang, Yi. “Problems in Learning under Limited Resources and Information.” 2017. Thesis, University of Illinois – Chicago. Accessed March 18, 2019. http://hdl.handle.net/10027/21988.

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

MLA Handbook (7th Edition):

Huang, Yi. “Problems in Learning under Limited Resources and Information.” 2017. Web. 18 Mar 2019.

Vancouver:

Huang Y. Problems in Learning under Limited Resources and Information. [Internet] [Thesis]. University of Illinois – Chicago; 2017. [cited 2019 Mar 18]. Available from: http://hdl.handle.net/10027/21988.

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

Council of Science Editors:

Huang Y. Problems in Learning under Limited Resources and Information. [Thesis]. University of Illinois – Chicago; 2017. Available from: http://hdl.handle.net/10027/21988

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


Université Catholique de Louvain

4. Gualberto Ferreira Coelho, Frederico. Semi-supervised feature selection.

Degree: 2013, Université Catholique de Louvain

As data acquisition has become relatively easy and inexpensive, data sets are becoming extremely large, both in the number of variables and in the number… (more)

Subjects/Keywords: Machine learning; Feature selection; Classification

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APA (6th Edition):

Gualberto Ferreira Coelho, F. (2013). Semi-supervised feature selection. (Thesis). Université Catholique de Louvain. Retrieved from http://hdl.handle.net/2078.1/128255

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):

Gualberto Ferreira Coelho, Frederico. “Semi-supervised feature selection.” 2013. Thesis, Université Catholique de Louvain. Accessed March 18, 2019. http://hdl.handle.net/2078.1/128255.

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

MLA Handbook (7th Edition):

Gualberto Ferreira Coelho, Frederico. “Semi-supervised feature selection.” 2013. Web. 18 Mar 2019.

Vancouver:

Gualberto Ferreira Coelho F. Semi-supervised feature selection. [Internet] [Thesis]. Université Catholique de Louvain; 2013. [cited 2019 Mar 18]. Available from: http://hdl.handle.net/2078.1/128255.

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

Council of Science Editors:

Gualberto Ferreira Coelho F. Semi-supervised feature selection. [Thesis]. Université Catholique de Louvain; 2013. Available from: http://hdl.handle.net/2078.1/128255

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


Case Western Reserve University

5. Latham, Andrew C. Multiple-Instance Feature Ranking.

Degree: MSs, EECS - Electrical Engineering, 2016, Case Western Reserve University

 Multiple-instance learning is a subfield of machine learning in which training data is provided as labeled sets of instances called "bags," with the instance labels… (more)

Subjects/Keywords: Computer Science; Machine Learning; Feature Selection; Feature Ranking; Multiple-Instance Learning

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APA (6th Edition):

Latham, A. C. (2016). Multiple-Instance Feature Ranking. (Masters Thesis). Case Western Reserve University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=case1440642294

Chicago Manual of Style (16th Edition):

Latham, Andrew C. “Multiple-Instance Feature Ranking.” 2016. Masters Thesis, Case Western Reserve University. Accessed March 18, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=case1440642294.

MLA Handbook (7th Edition):

Latham, Andrew C. “Multiple-Instance Feature Ranking.” 2016. Web. 18 Mar 2019.

Vancouver:

Latham AC. Multiple-Instance Feature Ranking. [Internet] [Masters thesis]. Case Western Reserve University; 2016. [cited 2019 Mar 18]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=case1440642294.

Council of Science Editors:

Latham AC. Multiple-Instance Feature Ranking. [Masters Thesis]. Case Western Reserve University; 2016. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=case1440642294


University of Cincinnati

6. Guo, Xinyu. Improved Feature-Selection for Classification Problems using Multiple Auto-Encoders.

Degree: PhD, Engineering and Applied Science: Computer Science and Engineering, 2018, University of Cincinnati

 Complex and high-dimensional data such as medical images, sensor measurements, and sounds is oftenlimited. In machine learning, using such datasets to directly train classification algorithms… (more)

Subjects/Keywords: Computer Science; auto-encoder; feature selection; feature learning; deep learning; neuroimaging

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

APA (6th Edition):

Guo, X. (2018). Improved Feature-Selection for Classification Problems using Multiple Auto-Encoders. (Doctoral Dissertation). University of Cincinnati. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=ucin1522420335154157

Chicago Manual of Style (16th Edition):

Guo, Xinyu. “Improved Feature-Selection for Classification Problems using Multiple Auto-Encoders.” 2018. Doctoral Dissertation, University of Cincinnati. Accessed March 18, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1522420335154157.

MLA Handbook (7th Edition):

Guo, Xinyu. “Improved Feature-Selection for Classification Problems using Multiple Auto-Encoders.” 2018. Web. 18 Mar 2019.

Vancouver:

Guo X. Improved Feature-Selection for Classification Problems using Multiple Auto-Encoders. [Internet] [Doctoral dissertation]. University of Cincinnati; 2018. [cited 2019 Mar 18]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ucin1522420335154157.

Council of Science Editors:

Guo X. Improved Feature-Selection for Classification Problems using Multiple Auto-Encoders. [Doctoral Dissertation]. University of Cincinnati; 2018. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ucin1522420335154157


Northeastern University

7. Wang, Shuyang. Face representation learning and its applications on social media.

Degree: PhD, Department of Electrical and Computer Engineering, 2018, Northeastern University

Learning and extracting good feature representations for face images is always a hot topic in machine learning field, especially in this era of social media… (more)

Subjects/Keywords: feature learning; machine learning; social media

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APA (6th Edition):

Wang, S. (2018). Face representation learning and its applications on social media. (Doctoral Dissertation). Northeastern University. Retrieved from http://hdl.handle.net/2047/D20287864

Chicago Manual of Style (16th Edition):

Wang, Shuyang. “Face representation learning and its applications on social media.” 2018. Doctoral Dissertation, Northeastern University. Accessed March 18, 2019. http://hdl.handle.net/2047/D20287864.

MLA Handbook (7th Edition):

Wang, Shuyang. “Face representation learning and its applications on social media.” 2018. Web. 18 Mar 2019.

Vancouver:

Wang S. Face representation learning and its applications on social media. [Internet] [Doctoral dissertation]. Northeastern University; 2018. [cited 2019 Mar 18]. Available from: http://hdl.handle.net/2047/D20287864.

Council of Science Editors:

Wang S. Face representation learning and its applications on social media. [Doctoral Dissertation]. Northeastern University; 2018. Available from: http://hdl.handle.net/2047/D20287864


University of Connecticut

8. Yankee, Tara N. Rank Aggregation of Feature Scoring Methods for Unsupervised Learning.

Degree: M. Eng., Biomedical Engineering, 2017, University of Connecticut

  The ability to collect and store large amounts of data is transforming data-driven discovery; recent technological advances in biology allow systematic data production and… (more)

Subjects/Keywords: clustering; ensemble learning; feature selection; unsupervised learning

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APA (6th Edition):

Yankee, T. N. (2017). Rank Aggregation of Feature Scoring Methods for Unsupervised Learning. (Masters Thesis). University of Connecticut. Retrieved from https://opencommons.uconn.edu/gs_theses/1123

Chicago Manual of Style (16th Edition):

Yankee, Tara N. “Rank Aggregation of Feature Scoring Methods for Unsupervised Learning.” 2017. Masters Thesis, University of Connecticut. Accessed March 18, 2019. https://opencommons.uconn.edu/gs_theses/1123.

MLA Handbook (7th Edition):

Yankee, Tara N. “Rank Aggregation of Feature Scoring Methods for Unsupervised Learning.” 2017. Web. 18 Mar 2019.

Vancouver:

Yankee TN. Rank Aggregation of Feature Scoring Methods for Unsupervised Learning. [Internet] [Masters thesis]. University of Connecticut; 2017. [cited 2019 Mar 18]. Available from: https://opencommons.uconn.edu/gs_theses/1123.

Council of Science Editors:

Yankee TN. Rank Aggregation of Feature Scoring Methods for Unsupervised Learning. [Masters Thesis]. University of Connecticut; 2017. Available from: https://opencommons.uconn.edu/gs_theses/1123


Wayne State University

9. Xu, Haotian. Representation Learning With Convolutional Neural Networks.

Degree: PhD, Computer Science, 2018, Wayne State University

  Deep learning methods have achieved great success in the areas of Computer Vision and Natural Language Processing. Recently, the rapidly developing field of deep… (more)

Subjects/Keywords: deep learning; feature learning; Computer Sciences

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APA (6th Edition):

Xu, H. (2018). Representation Learning With Convolutional Neural Networks. (Doctoral Dissertation). Wayne State University. Retrieved from https://digitalcommons.wayne.edu/oa_dissertations/2133

Chicago Manual of Style (16th Edition):

Xu, Haotian. “Representation Learning With Convolutional Neural Networks.” 2018. Doctoral Dissertation, Wayne State University. Accessed March 18, 2019. https://digitalcommons.wayne.edu/oa_dissertations/2133.

MLA Handbook (7th Edition):

Xu, Haotian. “Representation Learning With Convolutional Neural Networks.” 2018. Web. 18 Mar 2019.

Vancouver:

Xu H. Representation Learning With Convolutional Neural Networks. [Internet] [Doctoral dissertation]. Wayne State University; 2018. [cited 2019 Mar 18]. Available from: https://digitalcommons.wayne.edu/oa_dissertations/2133.

Council of Science Editors:

Xu H. Representation Learning With Convolutional Neural Networks. [Doctoral Dissertation]. Wayne State University; 2018. Available from: https://digitalcommons.wayne.edu/oa_dissertations/2133


University of Ottawa

10. Niu, Teng. Sentiment Analysis on Multi-view Social Data .

Degree: 2016, University of Ottawa

 With the proliferation of social networks, people are likely to share their opinions about news, social events and products on the Web. There is an… (more)

Subjects/Keywords: Sentiment analysis; social media; multi-view data; textual feature; visual feature; joint feature learning

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APA (6th Edition):

Niu, T. (2016). Sentiment Analysis on Multi-view Social Data . (Thesis). University of Ottawa. Retrieved from http://hdl.handle.net/10393/34218

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):

Niu, Teng. “Sentiment Analysis on Multi-view Social Data .” 2016. Thesis, University of Ottawa. Accessed March 18, 2019. http://hdl.handle.net/10393/34218.

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

MLA Handbook (7th Edition):

Niu, Teng. “Sentiment Analysis on Multi-view Social Data .” 2016. Web. 18 Mar 2019.

Vancouver:

Niu T. Sentiment Analysis on Multi-view Social Data . [Internet] [Thesis]. University of Ottawa; 2016. [cited 2019 Mar 18]. Available from: http://hdl.handle.net/10393/34218.

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

Council of Science Editors:

Niu T. Sentiment Analysis on Multi-view Social Data . [Thesis]. University of Ottawa; 2016. Available from: http://hdl.handle.net/10393/34218

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


University of Tennessee – Knoxville

11. Luo, Jiajia. Feature Extraction and Recognition for Human Action Recognition.

Degree: 2014, University of Tennessee – Knoxville

 How to automatically label videos containing human motions is the task of human action recognition. Traditional human action recognition algorithms use the RGB videos as… (more)

Subjects/Keywords: feature extraction; feature representation; dictionary learning; sparse coding; Other Computer Engineering

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APA (6th Edition):

Luo, J. (2014). Feature Extraction and Recognition for Human Action Recognition. (Doctoral Dissertation). University of Tennessee – Knoxville. Retrieved from https://trace.tennessee.edu/utk_graddiss/2710

Chicago Manual of Style (16th Edition):

Luo, Jiajia. “Feature Extraction and Recognition for Human Action Recognition.” 2014. Doctoral Dissertation, University of Tennessee – Knoxville. Accessed March 18, 2019. https://trace.tennessee.edu/utk_graddiss/2710.

MLA Handbook (7th Edition):

Luo, Jiajia. “Feature Extraction and Recognition for Human Action Recognition.” 2014. Web. 18 Mar 2019.

Vancouver:

Luo J. Feature Extraction and Recognition for Human Action Recognition. [Internet] [Doctoral dissertation]. University of Tennessee – Knoxville; 2014. [cited 2019 Mar 18]. Available from: https://trace.tennessee.edu/utk_graddiss/2710.

Council of Science Editors:

Luo J. Feature Extraction and Recognition for Human Action Recognition. [Doctoral Dissertation]. University of Tennessee – Knoxville; 2014. Available from: https://trace.tennessee.edu/utk_graddiss/2710


Victoria University of Wellington

12. Neshatian, Kourosh. Feature Manipulation with Genetic Programming.

Degree: 2010, Victoria University of Wellington

Feature manipulation refers to the process by which the input space of a machine learning task is altered in order to improve the learning quality… (more)

Subjects/Keywords: Machine learning; Evolutionary algorithms; Feature selection; Feature construction

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APA (6th Edition):

Neshatian, K. (2010). Feature Manipulation with Genetic Programming. (Doctoral Dissertation). Victoria University of Wellington. Retrieved from http://hdl.handle.net/10063/4425

Chicago Manual of Style (16th Edition):

Neshatian, Kourosh. “Feature Manipulation with Genetic Programming.” 2010. Doctoral Dissertation, Victoria University of Wellington. Accessed March 18, 2019. http://hdl.handle.net/10063/4425.

MLA Handbook (7th Edition):

Neshatian, Kourosh. “Feature Manipulation with Genetic Programming.” 2010. Web. 18 Mar 2019.

Vancouver:

Neshatian K. Feature Manipulation with Genetic Programming. [Internet] [Doctoral dissertation]. Victoria University of Wellington; 2010. [cited 2019 Mar 18]. Available from: http://hdl.handle.net/10063/4425.

Council of Science Editors:

Neshatian K. Feature Manipulation with Genetic Programming. [Doctoral Dissertation]. Victoria University of Wellington; 2010. Available from: http://hdl.handle.net/10063/4425


Princeton University

13. Wang, Yun. Feature Screening for the Lasso .

Degree: PhD, 2015, Princeton University

 Recently, the sparse representation of data with respect to a dictionary of features has contributed to successful new methods in machine learning, pattern analysis, statistics… (more)

Subjects/Keywords: classification; feature screening; feature selection; lasso; machine learning; sparse representation/regression

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APA (6th Edition):

Wang, Y. (2015). Feature Screening for the Lasso . (Doctoral Dissertation). Princeton University. Retrieved from http://arks.princeton.edu/ark:/88435/dsp01hq37vq979

Chicago Manual of Style (16th Edition):

Wang, Yun. “Feature Screening for the Lasso .” 2015. Doctoral Dissertation, Princeton University. Accessed March 18, 2019. http://arks.princeton.edu/ark:/88435/dsp01hq37vq979.

MLA Handbook (7th Edition):

Wang, Yun. “Feature Screening for the Lasso .” 2015. Web. 18 Mar 2019.

Vancouver:

Wang Y. Feature Screening for the Lasso . [Internet] [Doctoral dissertation]. Princeton University; 2015. [cited 2019 Mar 18]. Available from: http://arks.princeton.edu/ark:/88435/dsp01hq37vq979.

Council of Science Editors:

Wang Y. Feature Screening for the Lasso . [Doctoral Dissertation]. Princeton University; 2015. Available from: http://arks.princeton.edu/ark:/88435/dsp01hq37vq979


Texas A&M University

14. Dean, Noah J. Observational Learning of a Bimanual Coordination Task: Understanding Movement Feature Extraction, Model Performance Level, and Perspective Angle.

Degree: 2010, Texas A&M University

 One experiment was adminstered to address three issues central to identifying the processes that underlie our ability to learn through observation. One objective of the… (more)

Subjects/Keywords: Relative Phase; Observational Learning; Movement Feature

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APA (6th Edition):

Dean, N. J. (2010). Observational Learning of a Bimanual Coordination Task: Understanding Movement Feature Extraction, Model Performance Level, and Perspective Angle. (Thesis). Texas A&M University. Retrieved from http://hdl.handle.net/1969.1/ETD-TAMU-2009-12-7327

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):

Dean, Noah J. “Observational Learning of a Bimanual Coordination Task: Understanding Movement Feature Extraction, Model Performance Level, and Perspective Angle.” 2010. Thesis, Texas A&M University. Accessed March 18, 2019. http://hdl.handle.net/1969.1/ETD-TAMU-2009-12-7327.

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

MLA Handbook (7th Edition):

Dean, Noah J. “Observational Learning of a Bimanual Coordination Task: Understanding Movement Feature Extraction, Model Performance Level, and Perspective Angle.” 2010. Web. 18 Mar 2019.

Vancouver:

Dean NJ. Observational Learning of a Bimanual Coordination Task: Understanding Movement Feature Extraction, Model Performance Level, and Perspective Angle. [Internet] [Thesis]. Texas A&M University; 2010. [cited 2019 Mar 18]. Available from: http://hdl.handle.net/1969.1/ETD-TAMU-2009-12-7327.

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

Council of Science Editors:

Dean NJ. Observational Learning of a Bimanual Coordination Task: Understanding Movement Feature Extraction, Model Performance Level, and Perspective Angle. [Thesis]. Texas A&M University; 2010. Available from: http://hdl.handle.net/1969.1/ETD-TAMU-2009-12-7327

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


University of Manchester

15. Pocock, Adam Craig. Feature selection via joint likelihood.

Degree: PhD, 2012, University of Manchester

 We study the nature of filter methods for feature selection. In particular, we examine information theoretic approaches to this problem, looking at the literature over… (more)

Subjects/Keywords: 006.3; machine learning; feature selection; information theory

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APA (6th Edition):

Pocock, A. C. (2012). Feature selection via joint likelihood. (Doctoral Dissertation). University of Manchester. Retrieved from https://www.research.manchester.ac.uk/portal/en/theses/feature-selection-via-joint-likelihood(3baba883-1fac-4658-bab0-164b54c3784a).html ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.558057

Chicago Manual of Style (16th Edition):

Pocock, Adam Craig. “Feature selection via joint likelihood.” 2012. Doctoral Dissertation, University of Manchester. Accessed March 18, 2019. https://www.research.manchester.ac.uk/portal/en/theses/feature-selection-via-joint-likelihood(3baba883-1fac-4658-bab0-164b54c3784a).html ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.558057.

MLA Handbook (7th Edition):

Pocock, Adam Craig. “Feature selection via joint likelihood.” 2012. Web. 18 Mar 2019.

Vancouver:

Pocock AC. Feature selection via joint likelihood. [Internet] [Doctoral dissertation]. University of Manchester; 2012. [cited 2019 Mar 18]. Available from: https://www.research.manchester.ac.uk/portal/en/theses/feature-selection-via-joint-likelihood(3baba883-1fac-4658-bab0-164b54c3784a).html ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.558057.

Council of Science Editors:

Pocock AC. Feature selection via joint likelihood. [Doctoral Dissertation]. University of Manchester; 2012. Available from: https://www.research.manchester.ac.uk/portal/en/theses/feature-selection-via-joint-likelihood(3baba883-1fac-4658-bab0-164b54c3784a).html ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.558057


University of Kansas

16. Zhong, Yi. Feature selection and classification for high-dimensional biological data under cross-validation framework.

Degree: PhD, Biostatistics, 2018, University of Kansas

 This research focuses on using statistical learning methods on high-dimensional biological data analysis. In our implementation of high-dimensional biological data analysis, we primarily utilize the… (more)

Subjects/Keywords: Statistics; cross-validation; feature selection; statistical learning

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APA (6th Edition):

Zhong, Y. (2018). Feature selection and classification for high-dimensional biological data under cross-validation framework. (Doctoral Dissertation). University of Kansas. Retrieved from http://hdl.handle.net/1808/27072

Chicago Manual of Style (16th Edition):

Zhong, Yi. “Feature selection and classification for high-dimensional biological data under cross-validation framework.” 2018. Doctoral Dissertation, University of Kansas. Accessed March 18, 2019. http://hdl.handle.net/1808/27072.

MLA Handbook (7th Edition):

Zhong, Yi. “Feature selection and classification for high-dimensional biological data under cross-validation framework.” 2018. Web. 18 Mar 2019.

Vancouver:

Zhong Y. Feature selection and classification for high-dimensional biological data under cross-validation framework. [Internet] [Doctoral dissertation]. University of Kansas; 2018. [cited 2019 Mar 18]. Available from: http://hdl.handle.net/1808/27072.

Council of Science Editors:

Zhong Y. Feature selection and classification for high-dimensional biological data under cross-validation framework. [Doctoral Dissertation]. University of Kansas; 2018. Available from: http://hdl.handle.net/1808/27072


University of Saskatchewan

17. Ralhan, Amitoz Singh. A study on machine learning algorithms for fall detection and movement classification.

Degree: 2009, University of Saskatchewan

 Fall among the elderly is an important health issue. Fall detection and movement tracking techniques are therefore instrumental in dealing with this issue. This thesis… (more)

Subjects/Keywords: Machine Learning; Fall Detection; Feature Selection

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

APA (6th Edition):

Ralhan, A. S. (2009). A study on machine learning algorithms for fall detection and movement classification. (Thesis). University of Saskatchewan. Retrieved from http://hdl.handle.net/10388/etd-12222009-144628

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):

Ralhan, Amitoz Singh. “A study on machine learning algorithms for fall detection and movement classification.” 2009. Thesis, University of Saskatchewan. Accessed March 18, 2019. http://hdl.handle.net/10388/etd-12222009-144628.

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

MLA Handbook (7th Edition):

Ralhan, Amitoz Singh. “A study on machine learning algorithms for fall detection and movement classification.” 2009. Web. 18 Mar 2019.

Vancouver:

Ralhan AS. A study on machine learning algorithms for fall detection and movement classification. [Internet] [Thesis]. University of Saskatchewan; 2009. [cited 2019 Mar 18]. Available from: http://hdl.handle.net/10388/etd-12222009-144628.

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

Council of Science Editors:

Ralhan AS. A study on machine learning algorithms for fall detection and movement classification. [Thesis]. University of Saskatchewan; 2009. Available from: http://hdl.handle.net/10388/etd-12222009-144628

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


University of Sydney

18. De Deuge, Mark. Manifold Learning Approaches to Compressing Latent Spaces of Unsupervised Feature Hierarchies .

Degree: 2015, University of Sydney

 Field robots encounter dynamic unstructured environments containing a vast array of unique objects. In order to make sense of the world in which they are… (more)

Subjects/Keywords: deep; learning; compressing; feature; hierarchy; manifold

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APA (6th Edition):

De Deuge, M. (2015). Manifold Learning Approaches to Compressing Latent Spaces of Unsupervised Feature Hierarchies . (Thesis). University of Sydney. Retrieved from http://hdl.handle.net/2123/14551

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):

De Deuge, Mark. “Manifold Learning Approaches to Compressing Latent Spaces of Unsupervised Feature Hierarchies .” 2015. Thesis, University of Sydney. Accessed March 18, 2019. http://hdl.handle.net/2123/14551.

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

MLA Handbook (7th Edition):

De Deuge, Mark. “Manifold Learning Approaches to Compressing Latent Spaces of Unsupervised Feature Hierarchies .” 2015. Web. 18 Mar 2019.

Vancouver:

De Deuge M. Manifold Learning Approaches to Compressing Latent Spaces of Unsupervised Feature Hierarchies . [Internet] [Thesis]. University of Sydney; 2015. [cited 2019 Mar 18]. Available from: http://hdl.handle.net/2123/14551.

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

Council of Science Editors:

De Deuge M. Manifold Learning Approaches to Compressing Latent Spaces of Unsupervised Feature Hierarchies . [Thesis]. University of Sydney; 2015. Available from: http://hdl.handle.net/2123/14551

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


University of Houston

19. Xu, Yan. Unsupervised Discovery and Representation of Subspace Trends in Massive Biomedical Datasets.

Degree: Electrical and Computer Engineering, Department of, 2015, University of Houston

 The goal of this dissertation is to develop unsupervised algorithms for discovering previously unknown subspace trends in massive multivariate biomedical data sets without the benefit… (more)

Subjects/Keywords: trend; visualization; biomedical; unsupervised learning; feature selection

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APA (6th Edition):

Xu, Y. (2015). Unsupervised Discovery and Representation of Subspace Trends in Massive Biomedical Datasets. (Thesis). University of Houston. Retrieved from http://hdl.handle.net/10657/3672

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):

Xu, Yan. “Unsupervised Discovery and Representation of Subspace Trends in Massive Biomedical Datasets.” 2015. Thesis, University of Houston. Accessed March 18, 2019. http://hdl.handle.net/10657/3672.

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

MLA Handbook (7th Edition):

Xu, Yan. “Unsupervised Discovery and Representation of Subspace Trends in Massive Biomedical Datasets.” 2015. Web. 18 Mar 2019.

Vancouver:

Xu Y. Unsupervised Discovery and Representation of Subspace Trends in Massive Biomedical Datasets. [Internet] [Thesis]. University of Houston; 2015. [cited 2019 Mar 18]. Available from: http://hdl.handle.net/10657/3672.

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

Council of Science Editors:

Xu Y. Unsupervised Discovery and Representation of Subspace Trends in Massive Biomedical Datasets. [Thesis]. University of Houston; 2015. Available from: http://hdl.handle.net/10657/3672

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


Uppsala University

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

Degree: Statistics, 2016, Uppsala University

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

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

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APA (6th Edition):

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

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

Chicago Manual of Style (16th Edition):

Li, Qiongzhu. “Study of Single and Ensemble Machine Learning Models on Credit Data to Detect Underlying Non-performing Loans.” 2016. Thesis, Uppsala University. Accessed March 18, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-297080.

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

MLA Handbook (7th Edition):

Li, Qiongzhu. “Study of Single and Ensemble Machine Learning Models on Credit Data to Detect Underlying Non-performing Loans.” 2016. Web. 18 Mar 2019.

Vancouver:

Li Q. Study of Single and Ensemble Machine Learning Models on Credit Data to Detect Underlying Non-performing Loans. [Internet] [Thesis]. Uppsala University; 2016. [cited 2019 Mar 18]. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-297080.

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

Council of Science Editors:

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

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


University of Texas – Austin

21. Cai, Shiyao. Predicting rental listing popularity : 2 Sigma connect Renthop.

Degree: Statistics, 2017, University of Texas – Austin

 Renting a perfect apartment can be a hassle. There are plenty of features people care about when it comes to finding the apartment, such as… (more)

Subjects/Keywords: Data mining; XGBoost; Feature engineering; Machine learning

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

APA (6th Edition):

Cai, S. (2017). Predicting rental listing popularity : 2 Sigma connect Renthop. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/62803

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):

Cai, Shiyao. “Predicting rental listing popularity : 2 Sigma connect Renthop.” 2017. Thesis, University of Texas – Austin. Accessed March 18, 2019. http://hdl.handle.net/2152/62803.

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

MLA Handbook (7th Edition):

Cai, Shiyao. “Predicting rental listing popularity : 2 Sigma connect Renthop.” 2017. Web. 18 Mar 2019.

Vancouver:

Cai S. Predicting rental listing popularity : 2 Sigma connect Renthop. [Internet] [Thesis]. University of Texas – Austin; 2017. [cited 2019 Mar 18]. Available from: http://hdl.handle.net/2152/62803.

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

Council of Science Editors:

Cai S. Predicting rental listing popularity : 2 Sigma connect Renthop. [Thesis]. University of Texas – Austin; 2017. Available from: http://hdl.handle.net/2152/62803

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


University of Connecticut

22. McClanahan, Brian D. Location Inference of Social Media Posts at Hyper-Local Scale.

Degree: MS, Computer Science and Engineering, 2016, University of Connecticut

  This paper describes an approach to infer the location of a social media post at a hyper-local scale based on its content, conditional to… (more)

Subjects/Keywords: social media; feature selection; machine learning

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APA (6th Edition):

McClanahan, B. D. (2016). Location Inference of Social Media Posts at Hyper-Local Scale. (Masters Thesis). University of Connecticut. Retrieved from https://opencommons.uconn.edu/gs_theses/949

Chicago Manual of Style (16th Edition):

McClanahan, Brian D. “Location Inference of Social Media Posts at Hyper-Local Scale.” 2016. Masters Thesis, University of Connecticut. Accessed March 18, 2019. https://opencommons.uconn.edu/gs_theses/949.

MLA Handbook (7th Edition):

McClanahan, Brian D. “Location Inference of Social Media Posts at Hyper-Local Scale.” 2016. Web. 18 Mar 2019.

Vancouver:

McClanahan BD. Location Inference of Social Media Posts at Hyper-Local Scale. [Internet] [Masters thesis]. University of Connecticut; 2016. [cited 2019 Mar 18]. Available from: https://opencommons.uconn.edu/gs_theses/949.

Council of Science Editors:

McClanahan BD. Location Inference of Social Media Posts at Hyper-Local Scale. [Masters Thesis]. University of Connecticut; 2016. Available from: https://opencommons.uconn.edu/gs_theses/949


University of Bridgeport

23. Uddin, Muhammad Fahim. Enhanced Machine Learning Engine Engineering Using Innovative Blending, Tuning, and Feature Optimization .

Degree: 2019, University of Bridgeport

 Investigated into and motivated by Ensemble Machine Learning (ML) techniques, this thesis contributes to addressing performance, consistency, and integrity issues such as overfitting, underfitting, predictive… (more)

Subjects/Keywords: Algorithms; Machine learning; Feature engineering; Blending

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APA (6th Edition):

Uddin, M. F. (2019). Enhanced Machine Learning Engine Engineering Using Innovative Blending, Tuning, and Feature Optimization . (Thesis). University of Bridgeport. Retrieved from https://scholarworks.bridgeport.edu/xmlui/handle/123456789/4010

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):

Uddin, Muhammad Fahim. “Enhanced Machine Learning Engine Engineering Using Innovative Blending, Tuning, and Feature Optimization .” 2019. Thesis, University of Bridgeport. Accessed March 18, 2019. https://scholarworks.bridgeport.edu/xmlui/handle/123456789/4010.

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

MLA Handbook (7th Edition):

Uddin, Muhammad Fahim. “Enhanced Machine Learning Engine Engineering Using Innovative Blending, Tuning, and Feature Optimization .” 2019. Web. 18 Mar 2019.

Vancouver:

Uddin MF. Enhanced Machine Learning Engine Engineering Using Innovative Blending, Tuning, and Feature Optimization . [Internet] [Thesis]. University of Bridgeport; 2019. [cited 2019 Mar 18]. Available from: https://scholarworks.bridgeport.edu/xmlui/handle/123456789/4010.

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

Council of Science Editors:

Uddin MF. Enhanced Machine Learning Engine Engineering Using Innovative Blending, Tuning, and Feature Optimization . [Thesis]. University of Bridgeport; 2019. Available from: https://scholarworks.bridgeport.edu/xmlui/handle/123456789/4010

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


Miami University

24. Liu, Xian. Feature Detection from Mobile LiDAR Using Deep Learning.

Degree: Master of Computer Science, Computer Science & Software Engineering, 2019, Miami University

 Automated object detection from remotely sensed data in urban areas is a challenging task due to the complexity of urban scenes. Although recent advances in… (more)

Subjects/Keywords: Computer Science; Deep learning, LiDAR, Feature Detection

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

APA (6th Edition):

Liu, X. (2019). Feature Detection from Mobile LiDAR Using Deep Learning. (Masters Thesis). Miami University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=miami1552002747337465

Chicago Manual of Style (16th Edition):

Liu, Xian. “Feature Detection from Mobile LiDAR Using Deep Learning.” 2019. Masters Thesis, Miami University. Accessed March 18, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=miami1552002747337465.

MLA Handbook (7th Edition):

Liu, Xian. “Feature Detection from Mobile LiDAR Using Deep Learning.” 2019. Web. 18 Mar 2019.

Vancouver:

Liu X. Feature Detection from Mobile LiDAR Using Deep Learning. [Internet] [Masters thesis]. Miami University; 2019. [cited 2019 Mar 18]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=miami1552002747337465.

Council of Science Editors:

Liu X. Feature Detection from Mobile LiDAR Using Deep Learning. [Masters Thesis]. Miami University; 2019. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=miami1552002747337465


NSYSU

25. Tseng, Yi-Chia. An Unified Approach to Inverse Reinforcement Learning by Oppositive Demonstrations.

Degree: Master, Electrical Engineering, 2015, NSYSU

 Reinforcement learning (RL) techniques use a reward function to correct a learning agent to solve sequential decision making problems through interactions with a dynamic environment,… (more)

Subjects/Keywords: Apprenticeship Learning; Feature weight; Inverse Reinforcement learning; Reward function; Reinforcement learning

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APA (6th Edition):

Tseng, Y. (2015). An Unified Approach to Inverse Reinforcement Learning by Oppositive Demonstrations. (Thesis). NSYSU. Retrieved from http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0727115-130716

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):

Tseng, Yi-Chia. “An Unified Approach to Inverse Reinforcement Learning by Oppositive Demonstrations.” 2015. Thesis, NSYSU. Accessed March 18, 2019. http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0727115-130716.

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

MLA Handbook (7th Edition):

Tseng, Yi-Chia. “An Unified Approach to Inverse Reinforcement Learning by Oppositive Demonstrations.” 2015. Web. 18 Mar 2019.

Vancouver:

Tseng Y. An Unified Approach to Inverse Reinforcement Learning by Oppositive Demonstrations. [Internet] [Thesis]. NSYSU; 2015. [cited 2019 Mar 18]. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0727115-130716.

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

Council of Science Editors:

Tseng Y. An Unified Approach to Inverse Reinforcement Learning by Oppositive Demonstrations. [Thesis]. NSYSU; 2015. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0727115-130716

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


NSYSU

26. Xu, Rong-Fang. Optimizing Extreme Learning Machines for Supervised Learning Applications.

Degree: Master, Electrical Engineering, 2014, NSYSU

 This thesis is divided into two parts. The first part is a machine learning-based feature extraction method for regression problem. The second part is an… (more)

Subjects/Keywords: incremental learning; machine Learning; feature clustering; feature extraction; correlation coefficient; mutual information; extreme learning machine; least-square support vector machine

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APA (6th Edition):

Xu, R. (2014). Optimizing Extreme Learning Machines for Supervised Learning Applications. (Thesis). NSYSU. Retrieved from http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-1130114-120105

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):

Xu, Rong-Fang. “Optimizing Extreme Learning Machines for Supervised Learning Applications.” 2014. Thesis, NSYSU. Accessed March 18, 2019. http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-1130114-120105.

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

MLA Handbook (7th Edition):

Xu, Rong-Fang. “Optimizing Extreme Learning Machines for Supervised Learning Applications.” 2014. Web. 18 Mar 2019.

Vancouver:

Xu R. Optimizing Extreme Learning Machines for Supervised Learning Applications. [Internet] [Thesis]. NSYSU; 2014. [cited 2019 Mar 18]. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-1130114-120105.

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

Council of Science Editors:

Xu R. Optimizing Extreme Learning Machines for Supervised Learning Applications. [Thesis]. NSYSU; 2014. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-1130114-120105

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


Texas A&M University

27. Gopal, Kreshna. Efficient case-based reasoning through feature weighting, and its application in protein crystallography.

Degree: 2009, Texas A&M University

 Data preprocessing is critical for machine learning, data mining, and pattern recognition. In particular, selecting relevant and non-redundant features in highdimensional data is important to… (more)

Subjects/Keywords: Case-Based Reasoning; Nearest Neighbor Learning; Feature Selection; Feature Weighting; Protein Crystallography

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APA (6th Edition):

Gopal, K. (2009). Efficient case-based reasoning through feature weighting, and its application in protein crystallography. (Thesis). Texas A&M University. Retrieved from http://hdl.handle.net/1969.1/ETD-TAMU-1906

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):

Gopal, Kreshna. “Efficient case-based reasoning through feature weighting, and its application in protein crystallography.” 2009. Thesis, Texas A&M University. Accessed March 18, 2019. http://hdl.handle.net/1969.1/ETD-TAMU-1906.

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

MLA Handbook (7th Edition):

Gopal, Kreshna. “Efficient case-based reasoning through feature weighting, and its application in protein crystallography.” 2009. Web. 18 Mar 2019.

Vancouver:

Gopal K. Efficient case-based reasoning through feature weighting, and its application in protein crystallography. [Internet] [Thesis]. Texas A&M University; 2009. [cited 2019 Mar 18]. Available from: http://hdl.handle.net/1969.1/ETD-TAMU-1906.

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

Council of Science Editors:

Gopal K. Efficient case-based reasoning through feature weighting, and its application in protein crystallography. [Thesis]. Texas A&M University; 2009. Available from: http://hdl.handle.net/1969.1/ETD-TAMU-1906

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


Cornell University

28. Altwaijry, Hani Abdulaziz S. LEARNING TO MATCH IMAGES WITH KEYPOINTS AND DESCRIPTORS .

Degree: 2017, Cornell University

 Image matching is a fundamental problem in computer vision. In the context of feature-based correspondence matching, SIFT and its variants have long excelled in a… (more)

Subjects/Keywords: feature description; computer vision; neural networks; Computer science; machine learning; feature matching

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APA (6th Edition):

Altwaijry, H. A. S. (2017). LEARNING TO MATCH IMAGES WITH KEYPOINTS AND DESCRIPTORS . (Thesis). Cornell University. Retrieved from http://hdl.handle.net/1813/51552

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):

Altwaijry, Hani Abdulaziz S. “LEARNING TO MATCH IMAGES WITH KEYPOINTS AND DESCRIPTORS .” 2017. Thesis, Cornell University. Accessed March 18, 2019. http://hdl.handle.net/1813/51552.

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

MLA Handbook (7th Edition):

Altwaijry, Hani Abdulaziz S. “LEARNING TO MATCH IMAGES WITH KEYPOINTS AND DESCRIPTORS .” 2017. Web. 18 Mar 2019.

Vancouver:

Altwaijry HAS. LEARNING TO MATCH IMAGES WITH KEYPOINTS AND DESCRIPTORS . [Internet] [Thesis]. Cornell University; 2017. [cited 2019 Mar 18]. Available from: http://hdl.handle.net/1813/51552.

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

Council of Science Editors:

Altwaijry HAS. LEARNING TO MATCH IMAGES WITH KEYPOINTS AND DESCRIPTORS . [Thesis]. Cornell University; 2017. Available from: http://hdl.handle.net/1813/51552

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


University of Sydney

29. De Silva, Anthony Mihirana. Grammar based feature generation for time-series prediction .

Degree: 2013, University of Sydney

 The application of machine learning techniques to predict time-series continues to attract considerable attention due to the difficulty of the prediction problems compounded by the… (more)

Subjects/Keywords: Feature Generation; Time-series Prediction; Context-free Grammar; Grammatical Evolution; Feature Selection; Machine Learning

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

APA (6th Edition):

De Silva, A. M. (2013). Grammar based feature generation for time-series prediction . (Thesis). University of Sydney. Retrieved from http://hdl.handle.net/2123/10278

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):

De Silva, Anthony Mihirana. “Grammar based feature generation for time-series prediction .” 2013. Thesis, University of Sydney. Accessed March 18, 2019. http://hdl.handle.net/2123/10278.

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

MLA Handbook (7th Edition):

De Silva, Anthony Mihirana. “Grammar based feature generation for time-series prediction .” 2013. Web. 18 Mar 2019.

Vancouver:

De Silva AM. Grammar based feature generation for time-series prediction . [Internet] [Thesis]. University of Sydney; 2013. [cited 2019 Mar 18]. Available from: http://hdl.handle.net/2123/10278.

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

Council of Science Editors:

De Silva AM. Grammar based feature generation for time-series prediction . [Thesis]. University of Sydney; 2013. Available from: http://hdl.handle.net/2123/10278

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


Texas State University – San Marcos

30. Alabandi, Ghadeer Ahmed. Combining Deep Learning with Traditional Machine Learning to Improve Classification Accuracy on Small Datasets.

Degree: MS, Computer Science, 2017, Texas State University – San Marcos

Feature extraction and selection are essential phases in building machine learning classification models, and they have a great impact on the accuracy and the performance… (more)

Subjects/Keywords: Deep Learning; Machine learning; Feature extraction; Data mining

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APA (6th Edition):

Alabandi, G. A. (2017). Combining Deep Learning with Traditional Machine Learning to Improve Classification Accuracy on Small Datasets. (Masters Thesis). Texas State University – San Marcos. Retrieved from https://digital.library.txstate.edu/handle/10877/6923

Chicago Manual of Style (16th Edition):

Alabandi, Ghadeer Ahmed. “Combining Deep Learning with Traditional Machine Learning to Improve Classification Accuracy on Small Datasets.” 2017. Masters Thesis, Texas State University – San Marcos. Accessed March 18, 2019. https://digital.library.txstate.edu/handle/10877/6923.

MLA Handbook (7th Edition):

Alabandi, Ghadeer Ahmed. “Combining Deep Learning with Traditional Machine Learning to Improve Classification Accuracy on Small Datasets.” 2017. Web. 18 Mar 2019.

Vancouver:

Alabandi GA. Combining Deep Learning with Traditional Machine Learning to Improve Classification Accuracy on Small Datasets. [Internet] [Masters thesis]. Texas State University – San Marcos; 2017. [cited 2019 Mar 18]. Available from: https://digital.library.txstate.edu/handle/10877/6923.

Council of Science Editors:

Alabandi GA. Combining Deep Learning with Traditional Machine Learning to Improve Classification Accuracy on Small Datasets. [Masters Thesis]. Texas State University – San Marcos; 2017. Available from: https://digital.library.txstate.edu/handle/10877/6923

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