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You searched for subject:(Feature learning). Showing records 1 – 30 of 564 total matches.

[1] [2] [3] [4] [5] … [19]

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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 September 27, 2020. http://hdl.handle.net/10523/2360.

MLA Handbook (7th Edition):

Fu, Jie. “Learning Hierarchical Sparse Filters for Feature Matching .” 2012. Web. 27 Sep 2020.

Vancouver:

Fu J. Learning Hierarchical Sparse Filters for Feature Matching . [Internet] [Masters thesis]. University of Otago; 2012. [cited 2020 Sep 27]. 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 September 27, 2020. https://era.library.ualberta.ca/files/vt150k34n.

MLA Handbook (7th Edition):

KIRCI, MESUT. “Feature learning using state differences.” 2010. Web. 27 Sep 2020.

Vancouver:

KIRCI M. Feature learning using state differences. [Internet] [Masters thesis]. University of Alberta; 2010. [cited 2020 Sep 27]. 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 September 27, 2020. 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. 27 Sep 2020.

Vancouver:

Huang Y. Problems in Learning under Limited Resources and Information. [Internet] [Thesis]. University of Illinois – Chicago; 2017. [cited 2020 Sep 27]. 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 September 27, 2020. 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. 27 Sep 2020.

Vancouver:

Gualberto Ferreira Coelho F. Semi-supervised feature selection. [Internet] [Thesis]. Université Catholique de Louvain; 2013. [cited 2020 Sep 27]. 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 September 27, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=case1440642294.

MLA Handbook (7th Edition):

Latham, Andrew C. “Multiple-Instance Feature Ranking.” 2016. Web. 27 Sep 2020.

Vancouver:

Latham AC. Multiple-Instance Feature Ranking. [Internet] [Masters thesis]. Case Western Reserve University; 2016. [cited 2020 Sep 27]. 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 September 27, 2020. 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. 27 Sep 2020.

Vancouver:

Guo X. Improved Feature-Selection for Classification Problems using Multiple Auto-Encoders. [Internet] [Doctoral dissertation]. University of Cincinnati; 2018. [cited 2020 Sep 27]. 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


University of North Texas

7. Florescu, Corina Andreea. SurfKE: A Graph-Based Feature Learning Framework for Keyphrase Extraction.

Degree: 2019, University of North Texas

 Current unsupervised approaches for keyphrase extraction compute a single importance score for each candidate word by considering the number and quality of its associated words… (more)

Subjects/Keywords: keyphrase extraction; graph representation learning; feature learning

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APA (6th Edition):

Florescu, C. A. (2019). SurfKE: A Graph-Based Feature Learning Framework for Keyphrase Extraction. (Thesis). University of North Texas. Retrieved from https://digital.library.unt.edu/ark:/67531/metadc1538730/

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):

Florescu, Corina Andreea. “SurfKE: A Graph-Based Feature Learning Framework for Keyphrase Extraction.” 2019. Thesis, University of North Texas. Accessed September 27, 2020. https://digital.library.unt.edu/ark:/67531/metadc1538730/.

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

MLA Handbook (7th Edition):

Florescu, Corina Andreea. “SurfKE: A Graph-Based Feature Learning Framework for Keyphrase Extraction.” 2019. Web. 27 Sep 2020.

Vancouver:

Florescu CA. SurfKE: A Graph-Based Feature Learning Framework for Keyphrase Extraction. [Internet] [Thesis]. University of North Texas; 2019. [cited 2020 Sep 27]. Available from: https://digital.library.unt.edu/ark:/67531/metadc1538730/.

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

Council of Science Editors:

Florescu CA. SurfKE: A Graph-Based Feature Learning Framework for Keyphrase Extraction. [Thesis]. University of North Texas; 2019. Available from: https://digital.library.unt.edu/ark:/67531/metadc1538730/

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


Wayne State University

8. 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 September 27, 2020. https://digitalcommons.wayne.edu/oa_dissertations/2133.

MLA Handbook (7th Edition):

Xu, Haotian. “Representation Learning With Convolutional Neural Networks.” 2018. Web. 27 Sep 2020.

Vancouver:

Xu H. Representation Learning With Convolutional Neural Networks. [Internet] [Doctoral dissertation]. Wayne State University; 2018. [cited 2020 Sep 27]. 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 Connecticut

9. 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 September 27, 2020. 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. 27 Sep 2020.

Vancouver:

Yankee TN. Rank Aggregation of Feature Scoring Methods for Unsupervised Learning. [Internet] [Masters thesis]. University of Connecticut; 2017. [cited 2020 Sep 27]. 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


Northeastern University

10. 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 September 27, 2020. http://hdl.handle.net/2047/D20287864.

MLA Handbook (7th Edition):

Wang, Shuyang. “Face representation learning and its applications on social media.” 2018. Web. 27 Sep 2020.

Vancouver:

Wang S. Face representation learning and its applications on social media. [Internet] [Doctoral dissertation]. Northeastern University; 2018. [cited 2020 Sep 27]. 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 Ottawa

11. 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 September 27, 2020. 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. 27 Sep 2020.

Vancouver:

Niu T. Sentiment Analysis on Multi-view Social Data . [Internet] [Thesis]. University of Ottawa; 2016. [cited 2020 Sep 27]. 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


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

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 September 27, 2020. http://hdl.handle.net/10063/4425.

MLA Handbook (7th Edition):

Neshatian, Kourosh. “Feature Manipulation with Genetic Programming.” 2010. Web. 27 Sep 2020.

Vancouver:

Neshatian K. Feature Manipulation with Genetic Programming. [Internet] [Doctoral dissertation]. Victoria University of Wellington; 2010. [cited 2020 Sep 27]. 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 September 27, 2020. http://arks.princeton.edu/ark:/88435/dsp01hq37vq979.

MLA Handbook (7th Edition):

Wang, Yun. “Feature Screening for the Lasso .” 2015. Web. 27 Sep 2020.

Vancouver:

Wang Y. Feature Screening for the Lasso . [Internet] [Doctoral dissertation]. Princeton University; 2015. [cited 2020 Sep 27]. 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


Delft University of Technology

14. Wang, Yuyang (author). An automated ECG signal quality assessment method with supervised learning algorithm.

Degree: 2018, Delft University of Technology

 Wearable health has become a striking area in our daily life. Electrocardiogram (ECG) is one of the biomedical signals collected by the wearable or portable… (more)

Subjects/Keywords: ECG signal; quality assessment; Supervised Learning; Feature extraction; Feature selection

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

APA (6th Edition):

Wang, Y. (. (2018). An automated ECG signal quality assessment method with supervised learning algorithm. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:41eab0f1-05a5-440c-a43b-f2f12b15a8aa

Chicago Manual of Style (16th Edition):

Wang, Yuyang (author). “An automated ECG signal quality assessment method with supervised learning algorithm.” 2018. Masters Thesis, Delft University of Technology. Accessed September 27, 2020. http://resolver.tudelft.nl/uuid:41eab0f1-05a5-440c-a43b-f2f12b15a8aa.

MLA Handbook (7th Edition):

Wang, Yuyang (author). “An automated ECG signal quality assessment method with supervised learning algorithm.” 2018. Web. 27 Sep 2020.

Vancouver:

Wang Y(. An automated ECG signal quality assessment method with supervised learning algorithm. [Internet] [Masters thesis]. Delft University of Technology; 2018. [cited 2020 Sep 27]. Available from: http://resolver.tudelft.nl/uuid:41eab0f1-05a5-440c-a43b-f2f12b15a8aa.

Council of Science Editors:

Wang Y(. An automated ECG signal quality assessment method with supervised learning algorithm. [Masters Thesis]. Delft University of Technology; 2018. Available from: http://resolver.tudelft.nl/uuid:41eab0f1-05a5-440c-a43b-f2f12b15a8aa


University of Tennessee – Knoxville

15. 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 September 27, 2020. https://trace.tennessee.edu/utk_graddiss/2710.

MLA Handbook (7th Edition):

Luo, Jiajia. “Feature Extraction and Recognition for Human Action Recognition.” 2014. Web. 27 Sep 2020.

Vancouver:

Luo J. Feature Extraction and Recognition for Human Action Recognition. [Internet] [Doctoral dissertation]. University of Tennessee – Knoxville; 2014. [cited 2020 Sep 27]. 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


University of Waterloo

16. lin, zhong-qiu. Quantifying the Performance of Explainability Algorithms.

Degree: 2020, University of Waterloo

 Given the complexity of the deep neural network (DNN), DNN has long been criticized for its lack of interpretability in its decision-making process. This 'black… (more)

Subjects/Keywords: deep learning; xai; explainable ai; feature importance

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APA (6th Edition):

lin, z. (2020). Quantifying the Performance of Explainability Algorithms. (Thesis). University of Waterloo. Retrieved from http://hdl.handle.net/10012/15922

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

Chicago Manual of Style (16th Edition):

lin, zhong-qiu. “Quantifying the Performance of Explainability Algorithms.” 2020. Thesis, University of Waterloo. Accessed September 27, 2020. http://hdl.handle.net/10012/15922.

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

MLA Handbook (7th Edition):

lin, zhong-qiu. “Quantifying the Performance of Explainability Algorithms.” 2020. Web. 27 Sep 2020.

Vancouver:

lin z. Quantifying the Performance of Explainability Algorithms. [Internet] [Thesis]. University of Waterloo; 2020. [cited 2020 Sep 27]. Available from: http://hdl.handle.net/10012/15922.

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

Council of Science Editors:

lin z. Quantifying the Performance of Explainability Algorithms. [Thesis]. University of Waterloo; 2020. Available from: http://hdl.handle.net/10012/15922

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


Miami University

17. 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 (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 September 27, 2020. 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. 27 Sep 2020.

Vancouver:

Liu X. Feature Detection from Mobile LiDAR Using Deep Learning. [Internet] [Masters thesis]. Miami University; 2019. [cited 2020 Sep 27]. 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


University of Connecticut

18. 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 September 27, 2020. 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. 27 Sep 2020.

Vancouver:

McClanahan BD. Location Inference of Social Media Posts at Hyper-Local Scale. [Internet] [Masters thesis]. University of Connecticut; 2016. [cited 2020 Sep 27]. 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 Manchester

19. 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 September 27, 2020. 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. 27 Sep 2020.

Vancouver:

Pocock AC. Feature selection via joint likelihood. [Internet] [Doctoral dissertation]. University of Manchester; 2012. [cited 2020 Sep 27]. 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 Bridgeport

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

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 September 27, 2020. 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. 27 Sep 2020.

Vancouver:

Uddin MF. Enhanced Machine Learning Engine Engineering Using Innovative Blending, Tuning, and Feature Optimization . [Internet] [Thesis]. University of Bridgeport; 2019. [cited 2020 Sep 27]. 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


University of Texas – Austin

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

Degree: MSin Statistics, 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. (Masters Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/62803

Chicago Manual of Style (16th Edition):

Cai, Shiyao. “Predicting rental listing popularity : 2 Sigma connect Renthop.” 2017. Masters Thesis, University of Texas – Austin. Accessed September 27, 2020. http://hdl.handle.net/2152/62803.

MLA Handbook (7th Edition):

Cai, Shiyao. “Predicting rental listing popularity : 2 Sigma connect Renthop.” 2017. Web. 27 Sep 2020.

Vancouver:

Cai S. Predicting rental listing popularity : 2 Sigma connect Renthop. [Internet] [Masters thesis]. University of Texas – Austin; 2017. [cited 2020 Sep 27]. Available from: http://hdl.handle.net/2152/62803.

Council of Science Editors:

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


University of Sydney

22. 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 September 27, 2020. 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. 27 Sep 2020.

Vancouver:

De Deuge M. Manifold Learning Approaches to Compressing Latent Spaces of Unsupervised Feature Hierarchies . [Internet] [Thesis]. University of Sydney; 2015. [cited 2020 Sep 27]. 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


Uppsala University

23. 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 September 27, 2020. 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. 27 Sep 2020.

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 2020 Sep 27]. 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 Western Ontario

24. Brashears, Bailey N. The Effects of Feature Verbalizablity and Indirect Feedback on Implicit Category Learning.

Degree: 2019, University of Western Ontario

 This study consisted of two experiments intended to investigate the effects of varying factors on the use of verbal and implicit classification systems when learning(more)

Subjects/Keywords: Category learning; COVIS theory; feature verbalizablity

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

APA (6th Edition):

Brashears, B. N. (2019). The Effects of Feature Verbalizablity and Indirect Feedback on Implicit Category Learning. (Thesis). University of Western Ontario. Retrieved from https://ir.lib.uwo.ca/etd/6289

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):

Brashears, Bailey N. “The Effects of Feature Verbalizablity and Indirect Feedback on Implicit Category Learning.” 2019. Thesis, University of Western Ontario. Accessed September 27, 2020. https://ir.lib.uwo.ca/etd/6289.

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

MLA Handbook (7th Edition):

Brashears, Bailey N. “The Effects of Feature Verbalizablity and Indirect Feedback on Implicit Category Learning.” 2019. Web. 27 Sep 2020.

Vancouver:

Brashears BN. The Effects of Feature Verbalizablity and Indirect Feedback on Implicit Category Learning. [Internet] [Thesis]. University of Western Ontario; 2019. [cited 2020 Sep 27]. Available from: https://ir.lib.uwo.ca/etd/6289.

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

Council of Science Editors:

Brashears BN. The Effects of Feature Verbalizablity and Indirect Feedback on Implicit Category Learning. [Thesis]. University of Western Ontario; 2019. Available from: https://ir.lib.uwo.ca/etd/6289

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


University of Victoria

25. Sharma, Mridula. Evaluating and enhancing the security of cyber physical systems using machine learning approaches.

Degree: Department of Electrical and Computer Engineering, 2020, University of Victoria

 The main aim of this dissertation is to address the security issues of the physical layer of Cyber Physical Systems. The network security is first… (more)

Subjects/Keywords: CPS; Supervised Machine Learning; RPL; Feature Selection

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

APA (6th Edition):

Sharma, M. (2020). Evaluating and enhancing the security of cyber physical systems using machine learning approaches. (Thesis). University of Victoria. Retrieved from http://hdl.handle.net/1828/11675

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):

Sharma, Mridula. “Evaluating and enhancing the security of cyber physical systems using machine learning approaches.” 2020. Thesis, University of Victoria. Accessed September 27, 2020. http://hdl.handle.net/1828/11675.

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

MLA Handbook (7th Edition):

Sharma, Mridula. “Evaluating and enhancing the security of cyber physical systems using machine learning approaches.” 2020. Web. 27 Sep 2020.

Vancouver:

Sharma M. Evaluating and enhancing the security of cyber physical systems using machine learning approaches. [Internet] [Thesis]. University of Victoria; 2020. [cited 2020 Sep 27]. Available from: http://hdl.handle.net/1828/11675.

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

Council of Science Editors:

Sharma M. Evaluating and enhancing the security of cyber physical systems using machine learning approaches. [Thesis]. University of Victoria; 2020. Available from: http://hdl.handle.net/1828/11675

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


University of Kansas

26. 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 September 27, 2020. 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. 27 Sep 2020.

Vancouver:

Zhong Y. Feature selection and classification for high-dimensional biological data under cross-validation framework. [Internet] [Doctoral dissertation]. University of Kansas; 2018. [cited 2020 Sep 27]. 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

27. Ulfenborg, Josefin. Machine learning to predict enzymes’ optimal catalytic temperature .

Degree: Chalmers tekniska högskola / Institutionen för data och informationsteknik, 2020, Chalmers University of Technology

 Enzymes are proteins which operate as biological catalysts in chemical processes, for instance in biofuel production. The efficiency and sustainability of these processes may be… (more)

Subjects/Keywords: Structural bioinformatics; enzymes; machine learning; feature engineering

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APA (6th Edition):

Ulfenborg, J. (2020). Machine learning to predict enzymes’ optimal catalytic temperature . (Thesis). Chalmers University of Technology. Retrieved from http://hdl.handle.net/20.500.12380/301399

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):

Ulfenborg, Josefin. “Machine learning to predict enzymes’ optimal catalytic temperature .” 2020. Thesis, Chalmers University of Technology. Accessed September 27, 2020. http://hdl.handle.net/20.500.12380/301399.

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

MLA Handbook (7th Edition):

Ulfenborg, Josefin. “Machine learning to predict enzymes’ optimal catalytic temperature .” 2020. Web. 27 Sep 2020.

Vancouver:

Ulfenborg J. Machine learning to predict enzymes’ optimal catalytic temperature . [Internet] [Thesis]. Chalmers University of Technology; 2020. [cited 2020 Sep 27]. Available from: http://hdl.handle.net/20.500.12380/301399.

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

Council of Science Editors:

Ulfenborg J. Machine learning to predict enzymes’ optimal catalytic temperature . [Thesis]. Chalmers University of Technology; 2020. Available from: http://hdl.handle.net/20.500.12380/301399

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


Victoria University of Wellington

28. Lensen, Andrew. Evolutionary Feature Manipulation in Unsupervised Learning.

Degree: 2019, Victoria University of Wellington

 Unsupervised learning is a fundamental category of machine learning that works on data for which no pre-existing labels are available. Unlike in supervised learning, which… (more)

Subjects/Keywords: Evolutionary computation; Feature manipulation; Unsupervised learning; Genetic programming; Clustering; Manifold learning; Feature construction; Feature selection; Particle swarm optimisation

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APA (6th Edition):

Lensen, A. (2019). Evolutionary Feature Manipulation in Unsupervised Learning. (Doctoral Dissertation). Victoria University of Wellington. Retrieved from http://hdl.handle.net/10063/8617

Chicago Manual of Style (16th Edition):

Lensen, Andrew. “Evolutionary Feature Manipulation in Unsupervised Learning.” 2019. Doctoral Dissertation, Victoria University of Wellington. Accessed September 27, 2020. http://hdl.handle.net/10063/8617.

MLA Handbook (7th Edition):

Lensen, Andrew. “Evolutionary Feature Manipulation in Unsupervised Learning.” 2019. Web. 27 Sep 2020.

Vancouver:

Lensen A. Evolutionary Feature Manipulation in Unsupervised Learning. [Internet] [Doctoral dissertation]. Victoria University of Wellington; 2019. [cited 2020 Sep 27]. Available from: http://hdl.handle.net/10063/8617.

Council of Science Editors:

Lensen A. Evolutionary Feature Manipulation in Unsupervised Learning. [Doctoral Dissertation]. Victoria University of Wellington; 2019. Available from: http://hdl.handle.net/10063/8617


NSYSU

29. 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 September 27, 2020. 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. 27 Sep 2020.

Vancouver:

Tseng Y. An Unified Approach to Inverse Reinforcement Learning by Oppositive Demonstrations. [Internet] [Thesis]. NSYSU; 2015. [cited 2020 Sep 27]. 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


Delft University of Technology

30. Sakyi-Gyinae, Master (author). A Machine Learning Approach to Evaluating Aircraft Deviations from Planned Routes.

Degree: 2019, Delft University of Technology

 With the increasing trend in air traffic demand and evidence of large deviations from filed flight plans, airspace capacity is not being optimally utilized. In… (more)

Subjects/Keywords: Machine learning; Trajectory Prediction; Aircraft Deviations; Neural Network; LSTM; Airspace Demand Optimalization; feature design; Feature selection; feature selection method

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APA (6th Edition):

Sakyi-Gyinae, M. (. (2019). A Machine Learning Approach to Evaluating Aircraft Deviations from Planned Routes. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:274b4386-539a-4193-80e9-f120c8d4832e

Chicago Manual of Style (16th Edition):

Sakyi-Gyinae, Master (author). “A Machine Learning Approach to Evaluating Aircraft Deviations from Planned Routes.” 2019. Masters Thesis, Delft University of Technology. Accessed September 27, 2020. http://resolver.tudelft.nl/uuid:274b4386-539a-4193-80e9-f120c8d4832e.

MLA Handbook (7th Edition):

Sakyi-Gyinae, Master (author). “A Machine Learning Approach to Evaluating Aircraft Deviations from Planned Routes.” 2019. Web. 27 Sep 2020.

Vancouver:

Sakyi-Gyinae M(. A Machine Learning Approach to Evaluating Aircraft Deviations from Planned Routes. [Internet] [Masters thesis]. Delft University of Technology; 2019. [cited 2020 Sep 27]. Available from: http://resolver.tudelft.nl/uuid:274b4386-539a-4193-80e9-f120c8d4832e.

Council of Science Editors:

Sakyi-Gyinae M(. A Machine Learning Approach to Evaluating Aircraft Deviations from Planned Routes. [Masters Thesis]. Delft University of Technology; 2019. Available from: http://resolver.tudelft.nl/uuid:274b4386-539a-4193-80e9-f120c8d4832e

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