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You searched for subject:(Feature selection). Showing records 1 – 30 of 571 total matches.

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University of Manchester

1. Nogueira, Sarah. Quantifying the Stability of Feature Selection.

Degree: 2018, University of Manchester

Feature Selection is central to modern data science, from exploratory data analysis to predictive model-building. The "stability"of a feature selection algorithm refers to the robustness… (more)

Subjects/Keywords: Stability; Feature Selection; Variable Selection

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

APA (6th Edition):

Nogueira, S. (2018). Quantifying the Stability of Feature Selection. (Doctoral Dissertation). University of Manchester. Retrieved from http://www.manchester.ac.uk/escholar/uk-ac-man-scw:313287

Chicago Manual of Style (16th Edition):

Nogueira, Sarah. “Quantifying the Stability of Feature Selection.” 2018. Doctoral Dissertation, University of Manchester. Accessed May 27, 2019. http://www.manchester.ac.uk/escholar/uk-ac-man-scw:313287.

MLA Handbook (7th Edition):

Nogueira, Sarah. “Quantifying the Stability of Feature Selection.” 2018. Web. 27 May 2019.

Vancouver:

Nogueira S. Quantifying the Stability of Feature Selection. [Internet] [Doctoral dissertation]. University of Manchester; 2018. [cited 2019 May 27]. Available from: http://www.manchester.ac.uk/escholar/uk-ac-man-scw:313287.

Council of Science Editors:

Nogueira S. Quantifying the Stability of Feature Selection. [Doctoral Dissertation]. University of Manchester; 2018. Available from: http://www.manchester.ac.uk/escholar/uk-ac-man-scw:313287


University of Technology, Sydney

2. Ubaudi, Franco Alessandro. Assessing a feature's trustworthiness and two approaches to feature selection.

Degree: 2011, University of Technology, Sydney

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

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

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APA (6th Edition):

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

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

Chicago Manual of Style (16th Edition):

Ubaudi, Franco Alessandro. “Assessing a feature's trustworthiness and two approaches to feature selection.” 2011. Thesis, University of Technology, Sydney. Accessed May 27, 2019. http://hdl.handle.net/10453/23392.

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

MLA Handbook (7th Edition):

Ubaudi, Franco Alessandro. “Assessing a feature's trustworthiness and two approaches to feature selection.” 2011. Web. 27 May 2019.

Vancouver:

Ubaudi FA. Assessing a feature's trustworthiness and two approaches to feature selection. [Internet] [Thesis]. University of Technology, Sydney; 2011. [cited 2019 May 27]. Available from: http://hdl.handle.net/10453/23392.

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

Council of Science Editors:

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

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


Victoria University of Wellington

3. Nguyen, Bach Hoai. Evolutionary Computation for Feature Selection in Classification.

Degree: 2018, Victoria University of Wellington

 Classification aims to identify a class label of an instance according to the information from its characteristics or features. Unfortunately, many classification problems have a… (more)

Subjects/Keywords: Feature Selection; Evolutionary Computation; Classification

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APA (6th Edition):

Nguyen, B. H. (2018). Evolutionary Computation for Feature Selection in Classification. (Doctoral Dissertation). Victoria University of Wellington. Retrieved from http://hdl.handle.net/10063/7821

Chicago Manual of Style (16th Edition):

Nguyen, Bach Hoai. “Evolutionary Computation for Feature Selection in Classification.” 2018. Doctoral Dissertation, Victoria University of Wellington. Accessed May 27, 2019. http://hdl.handle.net/10063/7821.

MLA Handbook (7th Edition):

Nguyen, Bach Hoai. “Evolutionary Computation for Feature Selection in Classification.” 2018. Web. 27 May 2019.

Vancouver:

Nguyen BH. Evolutionary Computation for Feature Selection in Classification. [Internet] [Doctoral dissertation]. Victoria University of Wellington; 2018. [cited 2019 May 27]. Available from: http://hdl.handle.net/10063/7821.

Council of Science Editors:

Nguyen BH. Evolutionary Computation for Feature Selection in Classification. [Doctoral Dissertation]. Victoria University of Wellington; 2018. Available from: http://hdl.handle.net/10063/7821

4. Raheel, Saeed. L’Apprentissage artificiel pour la fouille de données multilingues : application à la classification automatique des documents arabes : Machine learning and the data mining of multilingual documents : application to the automatic classification of arabic documents.

Degree: Docteur es, Sciences de l'information et de la communication, 2010, Université Lumière – Lyon II

 La classification automatique des documents, une approche issue de l’apprentissage artificiel et de la fouille de textes, s’avère être très efficace pour l’organisation des ressources… (more)

Subjects/Keywords: Sélection d’attributs; Feature Selection

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APA (6th Edition):

Raheel, S. (2010). L’Apprentissage artificiel pour la fouille de données multilingues : application à la classification automatique des documents arabes : Machine learning and the data mining of multilingual documents : application to the automatic classification of arabic documents. (Doctoral Dissertation). Université Lumière – Lyon II. Retrieved from http://www.theses.fr/2010LYO20118

Chicago Manual of Style (16th Edition):

Raheel, Saeed. “L’Apprentissage artificiel pour la fouille de données multilingues : application à la classification automatique des documents arabes : Machine learning and the data mining of multilingual documents : application to the automatic classification of arabic documents.” 2010. Doctoral Dissertation, Université Lumière – Lyon II. Accessed May 27, 2019. http://www.theses.fr/2010LYO20118.

MLA Handbook (7th Edition):

Raheel, Saeed. “L’Apprentissage artificiel pour la fouille de données multilingues : application à la classification automatique des documents arabes : Machine learning and the data mining of multilingual documents : application to the automatic classification of arabic documents.” 2010. Web. 27 May 2019.

Vancouver:

Raheel S. L’Apprentissage artificiel pour la fouille de données multilingues : application à la classification automatique des documents arabes : Machine learning and the data mining of multilingual documents : application to the automatic classification of arabic documents. [Internet] [Doctoral dissertation]. Université Lumière – Lyon II; 2010. [cited 2019 May 27]. Available from: http://www.theses.fr/2010LYO20118.

Council of Science Editors:

Raheel S. L’Apprentissage artificiel pour la fouille de données multilingues : application à la classification automatique des documents arabes : Machine learning and the data mining of multilingual documents : application to the automatic classification of arabic documents. [Doctoral Dissertation]. Université Lumière – Lyon II; 2010. Available from: http://www.theses.fr/2010LYO20118


Université Catholique de Louvain

5. Tits, Cédric. Application of feature subset selection on meaningful features leads to promising results for spike sorting.

Degree: 2016, Université Catholique de Louvain

Understanding the visual system is a real interest since it contributes to one of the most important sense for human: the vision. To achieve this… (more)

Subjects/Keywords: Feature Subset Selection; Spike Sorting

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APA (6th Edition):

Tits, C. (2016). Application of feature subset selection on meaningful features leads to promising results for spike sorting. (Thesis). Université Catholique de Louvain. Retrieved from http://hdl.handle.net/2078.1/thesis:4588

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):

Tits, Cédric. “Application of feature subset selection on meaningful features leads to promising results for spike sorting.” 2016. Thesis, Université Catholique de Louvain. Accessed May 27, 2019. http://hdl.handle.net/2078.1/thesis:4588.

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

MLA Handbook (7th Edition):

Tits, Cédric. “Application of feature subset selection on meaningful features leads to promising results for spike sorting.” 2016. Web. 27 May 2019.

Vancouver:

Tits C. Application of feature subset selection on meaningful features leads to promising results for spike sorting. [Internet] [Thesis]. Université Catholique de Louvain; 2016. [cited 2019 May 27]. Available from: http://hdl.handle.net/2078.1/thesis:4588.

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

Council of Science Editors:

Tits C. Application of feature subset selection on meaningful features leads to promising results for spike sorting. [Thesis]. Université Catholique de Louvain; 2016. Available from: http://hdl.handle.net/2078.1/thesis:4588

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


Université Catholique de Louvain

6. 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 May 27, 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. 27 May 2019.

Vancouver:

Gualberto Ferreira Coelho F. Semi-supervised feature selection. [Internet] [Thesis]. Université Catholique de Louvain; 2013. [cited 2019 May 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


UCLA

7. Chang, Kung-Hua. Complementarity In Data Mining.

Degree: Computer Science, 2015, UCLA

 A learning problem involving classifiers and features usually has three components: representation, evaluation, and optimization. Contemporary research represents classifiers and features as initially given, and… (more)

Subjects/Keywords: Computer science; Ensemble Selection; Feature Selection

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APA (6th Edition):

Chang, K. (2015). Complementarity In Data Mining. (Thesis). UCLA. Retrieved from http://www.escholarship.org/uc/item/8zn4s7mj

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):

Chang, Kung-Hua. “Complementarity In Data Mining.” 2015. Thesis, UCLA. Accessed May 27, 2019. http://www.escholarship.org/uc/item/8zn4s7mj.

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

MLA Handbook (7th Edition):

Chang, Kung-Hua. “Complementarity In Data Mining.” 2015. Web. 27 May 2019.

Vancouver:

Chang K. Complementarity In Data Mining. [Internet] [Thesis]. UCLA; 2015. [cited 2019 May 27]. Available from: http://www.escholarship.org/uc/item/8zn4s7mj.

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

Council of Science Editors:

Chang K. Complementarity In Data Mining. [Thesis]. UCLA; 2015. Available from: http://www.escholarship.org/uc/item/8zn4s7mj

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


University of Manchester

8. Nogueira, Sarah. Quantifying the stability of feature selection.

Degree: PhD, 2018, University of Manchester

Feature Selection is central to modern data science, from exploratory data analysis to predictive model-building. The "stability"of a feature selection algorithm refers to the robustness… (more)

Subjects/Keywords: 004; Feature Selection; Stability; Variable Selection

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

APA (6th Edition):

Nogueira, S. (2018). Quantifying the stability of feature selection. (Doctoral Dissertation). University of Manchester. Retrieved from https://www.research.manchester.ac.uk/portal/en/theses/quantifying-the-stability-of-feature-selection(6b69098a-58ee-4182-9a30-693d714f0c9f).html ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.740328

Chicago Manual of Style (16th Edition):

Nogueira, Sarah. “Quantifying the stability of feature selection.” 2018. Doctoral Dissertation, University of Manchester. Accessed May 27, 2019. https://www.research.manchester.ac.uk/portal/en/theses/quantifying-the-stability-of-feature-selection(6b69098a-58ee-4182-9a30-693d714f0c9f).html ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.740328.

MLA Handbook (7th Edition):

Nogueira, Sarah. “Quantifying the stability of feature selection.” 2018. Web. 27 May 2019.

Vancouver:

Nogueira S. Quantifying the stability of feature selection. [Internet] [Doctoral dissertation]. University of Manchester; 2018. [cited 2019 May 27]. Available from: https://www.research.manchester.ac.uk/portal/en/theses/quantifying-the-stability-of-feature-selection(6b69098a-58ee-4182-9a30-693d714f0c9f).html ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.740328.

Council of Science Editors:

Nogueira S. Quantifying the stability of feature selection. [Doctoral Dissertation]. University of Manchester; 2018. Available from: https://www.research.manchester.ac.uk/portal/en/theses/quantifying-the-stability-of-feature-selection(6b69098a-58ee-4182-9a30-693d714f0c9f).html ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.740328


Addis Ababa University

9. ZEWDIE, MOSSIE. OPTIMAL FEATURE SELECTION FOR NETWORK INTRUSION DETECTION: A DATA MINING APPROACH .

Degree: 2012, Addis Ababa University

 The traditional approach in securing computer systems against cyber threats is designing mechanisms such as firewalls, authentication tools, and virtual private networks that create a… (more)

Subjects/Keywords: Cost sensitive feature selection; Cost insensitive feature selection; IGR; CFS.

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APA (6th Edition):

ZEWDIE, M. (2012). OPTIMAL FEATURE SELECTION FOR NETWORK INTRUSION DETECTION: A DATA MINING APPROACH . (Thesis). Addis Ababa University. Retrieved from http://etd.aau.edu.et/dspace/handle/123456789/2836

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):

ZEWDIE, MOSSIE. “OPTIMAL FEATURE SELECTION FOR NETWORK INTRUSION DETECTION: A DATA MINING APPROACH .” 2012. Thesis, Addis Ababa University. Accessed May 27, 2019. http://etd.aau.edu.et/dspace/handle/123456789/2836.

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

MLA Handbook (7th Edition):

ZEWDIE, MOSSIE. “OPTIMAL FEATURE SELECTION FOR NETWORK INTRUSION DETECTION: A DATA MINING APPROACH .” 2012. Web. 27 May 2019.

Vancouver:

ZEWDIE M. OPTIMAL FEATURE SELECTION FOR NETWORK INTRUSION DETECTION: A DATA MINING APPROACH . [Internet] [Thesis]. Addis Ababa University; 2012. [cited 2019 May 27]. Available from: http://etd.aau.edu.et/dspace/handle/123456789/2836.

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

Council of Science Editors:

ZEWDIE M. OPTIMAL FEATURE SELECTION FOR NETWORK INTRUSION DETECTION: A DATA MINING APPROACH . [Thesis]. Addis Ababa University; 2012. Available from: http://etd.aau.edu.et/dspace/handle/123456789/2836

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


McMaster University

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

Degree: PhD, 2017, McMaster University

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

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

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APA (6th Edition):

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

Chicago Manual of Style (16th Edition):

Armanfard, Narges. “Localized Feature Selection for Classification.” 2017. Doctoral Dissertation, McMaster University. Accessed May 27, 2019. http://hdl.handle.net/11375/20944.

MLA Handbook (7th Edition):

Armanfard, Narges. “Localized Feature Selection for Classification.” 2017. Web. 27 May 2019.

Vancouver:

Armanfard N. Localized Feature Selection for Classification. [Internet] [Doctoral dissertation]. McMaster University; 2017. [cited 2019 May 27]. Available from: http://hdl.handle.net/11375/20944.

Council of Science Editors:

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


University of Illinois – Chicago

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

Degree: 2017, University of Illinois – Chicago

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

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

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APA (6th Edition):

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

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

Chicago Manual of Style (16th Edition):

Wei, Xiaokai. “Unsupervised Feature Selection for Heterogeneous Data.” 2017. Thesis, University of Illinois – Chicago. Accessed May 27, 2019. http://hdl.handle.net/10027/21855.

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

MLA Handbook (7th Edition):

Wei, Xiaokai. “Unsupervised Feature Selection for Heterogeneous Data.” 2017. Web. 27 May 2019.

Vancouver:

Wei X. Unsupervised Feature Selection for Heterogeneous Data. [Internet] [Thesis]. University of Illinois – Chicago; 2017. [cited 2019 May 27]. Available from: http://hdl.handle.net/10027/21855.

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

Council of Science Editors:

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

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


University of Cincinnati

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

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

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

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

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

APA (6th Edition):

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

Chicago Manual of Style (16th Edition):

Zhang, Yi. “Application of Hyper-geometric Hypothesis-based Quantication and Markov Blanket Feature Selection Methods to Generate Signals for Adverse Drug Reaction Detection.” 2012. Masters Thesis, University of Cincinnati. Accessed May 27, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1353343669.

MLA Handbook (7th Edition):

Zhang, Yi. “Application of Hyper-geometric Hypothesis-based Quantication and Markov Blanket Feature Selection Methods to Generate Signals for Adverse Drug Reaction Detection.” 2012. Web. 27 May 2019.

Vancouver:

Zhang Y. Application of Hyper-geometric Hypothesis-based Quantication and Markov Blanket Feature Selection Methods to Generate Signals for Adverse Drug Reaction Detection. [Internet] [Masters thesis]. University of Cincinnati; 2012. [cited 2019 May 27]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ucin1353343669.

Council of Science Editors:

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


University of Manchester

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

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 May 27, 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. 27 May 2019.

Vancouver:

Pocock AC. Feature selection via joint likelihood. [Internet] [Doctoral dissertation]. University of Manchester; 2012. [cited 2019 May 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 Southern California

14. Tsau, Enshuo. Advanced features and feature selection methods for vibration and audio signal classification.

Degree: PhD, Electrical Engineering, 2012, University of Southern California

 An adequate feature set plays a key role in many signal classification and recognition applications. This is a challenging problem due to the nonlinearity and… (more)

Subjects/Keywords: CELP; fault diagnosis; feature selection; HHT

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APA (6th Edition):

Tsau, E. (2012). Advanced features and feature selection methods for vibration and audio signal classification. (Doctoral Dissertation). University of Southern California. Retrieved from http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/89597/rec/542

Chicago Manual of Style (16th Edition):

Tsau, Enshuo. “Advanced features and feature selection methods for vibration and audio signal classification.” 2012. Doctoral Dissertation, University of Southern California. Accessed May 27, 2019. http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/89597/rec/542.

MLA Handbook (7th Edition):

Tsau, Enshuo. “Advanced features and feature selection methods for vibration and audio signal classification.” 2012. Web. 27 May 2019.

Vancouver:

Tsau E. Advanced features and feature selection methods for vibration and audio signal classification. [Internet] [Doctoral dissertation]. University of Southern California; 2012. [cited 2019 May 27]. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/89597/rec/542.

Council of Science Editors:

Tsau E. Advanced features and feature selection methods for vibration and audio signal classification. [Doctoral Dissertation]. University of Southern California; 2012. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/89597/rec/542


University of Kansas

15. 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 May 27, 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. 27 May 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 May 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


University of Saskatchewan

16. 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 (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 May 27, 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. 27 May 2019.

Vancouver:

Ralhan AS. A study on machine learning algorithms for fall detection and movement classification. [Internet] [Thesis]. University of Saskatchewan; 2009. [cited 2019 May 27]. 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

17. Pham, Thuy Thi. Automated Classification for Biomedical Data: Machine Learning Approaches for Subject-Independent Settings .

Degree: 2017, University of Sydney

 This thesis advocates two main factors to improve subject-independent automated classification techniques: utilizing better feature extraction, and a more efficient model of classification. Supervised techniques… (more)

Subjects/Keywords: Subject-independent; automated classification; feature selection; anomaly

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APA (6th Edition):

Pham, T. T. (2017). Automated Classification for Biomedical Data: Machine Learning Approaches for Subject-Independent Settings . (Thesis). University of Sydney. Retrieved from http://hdl.handle.net/2123/17177

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):

Pham, Thuy Thi. “Automated Classification for Biomedical Data: Machine Learning Approaches for Subject-Independent Settings .” 2017. Thesis, University of Sydney. Accessed May 27, 2019. http://hdl.handle.net/2123/17177.

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

MLA Handbook (7th Edition):

Pham, Thuy Thi. “Automated Classification for Biomedical Data: Machine Learning Approaches for Subject-Independent Settings .” 2017. Web. 27 May 2019.

Vancouver:

Pham TT. Automated Classification for Biomedical Data: Machine Learning Approaches for Subject-Independent Settings . [Internet] [Thesis]. University of Sydney; 2017. [cited 2019 May 27]. Available from: http://hdl.handle.net/2123/17177.

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

Council of Science Editors:

Pham TT. Automated Classification for Biomedical Data: Machine Learning Approaches for Subject-Independent Settings . [Thesis]. University of Sydney; 2017. Available from: http://hdl.handle.net/2123/17177

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


University of Houston

18. 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 May 27, 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. 27 May 2019.

Vancouver:

Xu Y. Unsupervised Discovery and Representation of Subspace Trends in Massive Biomedical Datasets. [Internet] [Thesis]. University of Houston; 2015. [cited 2019 May 27]. 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


University of Connecticut

19. 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 May 27, 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. 27 May 2019.

Vancouver:

McClanahan BD. Location Inference of Social Media Posts at Hyper-Local Scale. [Internet] [Masters thesis]. University of Connecticut; 2016. [cited 2019 May 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 Connecticut

20. 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 May 27, 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. 27 May 2019.

Vancouver:

Yankee TN. Rank Aggregation of Feature Scoring Methods for Unsupervised Learning. [Internet] [Masters thesis]. University of Connecticut; 2017. [cited 2019 May 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


University of Rochester

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

Degree: PhD, 2014, University of Rochester

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

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

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APA (6th Edition):

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

Chicago Manual of Style (16th Edition):

Evans, Katie N.; Love, Tanzy. “Extensions to model-based clustering for mixed-type data : a new model framework, variable selection, and outlier detection.” 2014. Doctoral Dissertation, University of Rochester. Accessed May 27, 2019. http://hdl.handle.net/1802/28443.

MLA Handbook (7th Edition):

Evans, Katie N.; Love, Tanzy. “Extensions to model-based clustering for mixed-type data : a new model framework, variable selection, and outlier detection.” 2014. Web. 27 May 2019.

Vancouver:

Evans, Katie N.; Love T. Extensions to model-based clustering for mixed-type data : a new model framework, variable selection, and outlier detection. [Internet] [Doctoral dissertation]. University of Rochester; 2014. [cited 2019 May 27]. Available from: http://hdl.handle.net/1802/28443.

Council of Science Editors:

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


University of Southern California

22. Cho, Seong Ho. Block-based image steganalysis: algorithm and performance evaluation.

Degree: PhD, Electrical Engineering, 2012, University of Southern California

 Traditional image steganalysis techniques are conducted with respect to the entire image. In this work, we aim to differentiate a stego image from its cover… (more)

Subjects/Keywords: steganalysis; steganography; decision fusion; feature selection

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APA (6th Edition):

Cho, S. H. (2012). Block-based image steganalysis: algorithm and performance evaluation. (Doctoral Dissertation). University of Southern California. Retrieved from http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/81733/rec/1140

Chicago Manual of Style (16th Edition):

Cho, Seong Ho. “Block-based image steganalysis: algorithm and performance evaluation.” 2012. Doctoral Dissertation, University of Southern California. Accessed May 27, 2019. http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/81733/rec/1140.

MLA Handbook (7th Edition):

Cho, Seong Ho. “Block-based image steganalysis: algorithm and performance evaluation.” 2012. Web. 27 May 2019.

Vancouver:

Cho SH. Block-based image steganalysis: algorithm and performance evaluation. [Internet] [Doctoral dissertation]. University of Southern California; 2012. [cited 2019 May 27]. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/81733/rec/1140.

Council of Science Editors:

Cho SH. Block-based image steganalysis: algorithm and performance evaluation. [Doctoral Dissertation]. University of Southern California; 2012. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/81733/rec/1140


University of New South Wales

23. Hossain, Md Ali. Subspace Detection Approaches for Hyperspectral Image Classification.

Degree: Engineering & Information Technology Canberra, 2014, University of New South Wales

 Hyperspectral data provides rich information and is very useful for a range of applications from ground-cover types identification to target detection. With many benefits they… (more)

Subjects/Keywords: Feature selection; Image classification; Feature extraction; Mutual Information; Hyperspectral image; Feature reduction

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APA (6th Edition):

Hossain, M. A. (2014). Subspace Detection Approaches for Hyperspectral Image Classification. (Doctoral Dissertation). University of New South Wales. Retrieved from http://handle.unsw.edu.au/1959.4/53507 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:12202/SOURCE02?view=true

Chicago Manual of Style (16th Edition):

Hossain, Md Ali. “Subspace Detection Approaches for Hyperspectral Image Classification.” 2014. Doctoral Dissertation, University of New South Wales. Accessed May 27, 2019. http://handle.unsw.edu.au/1959.4/53507 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:12202/SOURCE02?view=true.

MLA Handbook (7th Edition):

Hossain, Md Ali. “Subspace Detection Approaches for Hyperspectral Image Classification.” 2014. Web. 27 May 2019.

Vancouver:

Hossain MA. Subspace Detection Approaches for Hyperspectral Image Classification. [Internet] [Doctoral dissertation]. University of New South Wales; 2014. [cited 2019 May 27]. Available from: http://handle.unsw.edu.au/1959.4/53507 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:12202/SOURCE02?view=true.

Council of Science Editors:

Hossain MA. Subspace Detection Approaches for Hyperspectral Image Classification. [Doctoral Dissertation]. University of New South Wales; 2014. Available from: http://handle.unsw.edu.au/1959.4/53507 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:12202/SOURCE02?view=true


NSYSU

24. Wang, Po-Cheng. Automatic Attribute Clustering and Feature Selection Based on Genetic Algorithms.

Degree: Master, Computer Science and Engineering, 2009, NSYSU

Feature selection is an important pre-processing step in mining and learning. A good set of features can not only improve the accuracy of classification, but… (more)

Subjects/Keywords: k-means; reduct; genetic algorithms; feature clustering; feature selection

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APA (6th Edition):

Wang, P. (2009). Automatic Attribute Clustering and Feature Selection Based on Genetic Algorithms. (Thesis). NSYSU. Retrieved from http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0821109-092325

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):

Wang, Po-Cheng. “Automatic Attribute Clustering and Feature Selection Based on Genetic Algorithms.” 2009. Thesis, NSYSU. Accessed May 27, 2019. http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0821109-092325.

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

MLA Handbook (7th Edition):

Wang, Po-Cheng. “Automatic Attribute Clustering and Feature Selection Based on Genetic Algorithms.” 2009. Web. 27 May 2019.

Vancouver:

Wang P. Automatic Attribute Clustering and Feature Selection Based on Genetic Algorithms. [Internet] [Thesis]. NSYSU; 2009. [cited 2019 May 27]. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0821109-092325.

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

Council of Science Editors:

Wang P. Automatic Attribute Clustering and Feature Selection Based on Genetic Algorithms. [Thesis]. NSYSU; 2009. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0821109-092325

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


Case Western Reserve University

25. 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 May 27, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=case1440642294.

MLA Handbook (7th Edition):

Latham, Andrew C. “Multiple-Instance Feature Ranking.” 2016. Web. 27 May 2019.

Vancouver:

Latham AC. Multiple-Instance Feature Ranking. [Internet] [Masters thesis]. Case Western Reserve University; 2016. [cited 2019 May 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


Arizona State University

26. Yamak, Didem. Characterization of Coronary Atherosclerotic Plaques by Dual Energy Computed Tomography.

Degree: PhD, Bioengineering, 2013, Arizona State University

 Coronary heart disease (CHD) is the most prevalent cause of death worldwide. Atherosclerosis which is the condition of plaque buildup on the inside of the… (more)

Subjects/Keywords: Biomedical engineering; Atherosclerosis; Dual Energy Computed Tomography; feature extraction; feature selection

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APA (6th Edition):

Yamak, D. (2013). Characterization of Coronary Atherosclerotic Plaques by Dual Energy Computed Tomography. (Doctoral Dissertation). Arizona State University. Retrieved from http://repository.asu.edu/items/18027

Chicago Manual of Style (16th Edition):

Yamak, Didem. “Characterization of Coronary Atherosclerotic Plaques by Dual Energy Computed Tomography.” 2013. Doctoral Dissertation, Arizona State University. Accessed May 27, 2019. http://repository.asu.edu/items/18027.

MLA Handbook (7th Edition):

Yamak, Didem. “Characterization of Coronary Atherosclerotic Plaques by Dual Energy Computed Tomography.” 2013. Web. 27 May 2019.

Vancouver:

Yamak D. Characterization of Coronary Atherosclerotic Plaques by Dual Energy Computed Tomography. [Internet] [Doctoral dissertation]. Arizona State University; 2013. [cited 2019 May 27]. Available from: http://repository.asu.edu/items/18027.

Council of Science Editors:

Yamak D. Characterization of Coronary Atherosclerotic Plaques by Dual Energy Computed Tomography. [Doctoral Dissertation]. Arizona State University; 2013. Available from: http://repository.asu.edu/items/18027


Victoria University of Wellington

27. Tran, Binh Ngan. Evolutionary Computation for Feature Manipulation in Classification on High-dimensional Data.

Degree: 2018, Victoria University of Wellington

 More and more high-dimensional data appears in machine learning, especially in classification tasks. With thousands of features, these datasets bring challenges to learning algorithms not… (more)

Subjects/Keywords: Evolutionary Computation; Feature selection; Feature construction; Classification; High-dimensional data

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APA (6th Edition):

Tran, B. N. (2018). Evolutionary Computation for Feature Manipulation in Classification on High-dimensional Data. (Doctoral Dissertation). Victoria University of Wellington. Retrieved from http://hdl.handle.net/10063/7078

Chicago Manual of Style (16th Edition):

Tran, Binh Ngan. “Evolutionary Computation for Feature Manipulation in Classification on High-dimensional Data.” 2018. Doctoral Dissertation, Victoria University of Wellington. Accessed May 27, 2019. http://hdl.handle.net/10063/7078.

MLA Handbook (7th Edition):

Tran, Binh Ngan. “Evolutionary Computation for Feature Manipulation in Classification on High-dimensional Data.” 2018. Web. 27 May 2019.

Vancouver:

Tran BN. Evolutionary Computation for Feature Manipulation in Classification on High-dimensional Data. [Internet] [Doctoral dissertation]. Victoria University of Wellington; 2018. [cited 2019 May 27]. Available from: http://hdl.handle.net/10063/7078.

Council of Science Editors:

Tran BN. Evolutionary Computation for Feature Manipulation in Classification on High-dimensional Data. [Doctoral Dissertation]. Victoria University of Wellington; 2018. Available from: http://hdl.handle.net/10063/7078


Victoria University of Wellington

28. 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 May 27, 2019. http://hdl.handle.net/10063/4425.

MLA Handbook (7th Edition):

Neshatian, Kourosh. “Feature Manipulation with Genetic Programming.” 2010. Web. 27 May 2019.

Vancouver:

Neshatian K. Feature Manipulation with Genetic Programming. [Internet] [Doctoral dissertation]. Victoria University of Wellington; 2010. [cited 2019 May 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

29. 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 May 27, 2019. http://arks.princeton.edu/ark:/88435/dsp01hq37vq979.

MLA Handbook (7th Edition):

Wang, Yun. “Feature Screening for the Lasso .” 2015. Web. 27 May 2019.

Vancouver:

Wang Y. Feature Screening for the Lasso . [Internet] [Doctoral dissertation]. Princeton University; 2015. [cited 2019 May 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


University of Windsor

30. Zhou, Tong. Automated Identification of Computer Science Research Papers.

Degree: MS, Computer Science, 2016, University of Windsor

 The fast growing speed of the size of scholarly data have made it necessary to nd out e cient machine learning ways to automatically categorize… (more)

Subjects/Keywords: academic papers; feature selection; feature weight normalization; language models; text classification

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APA (6th Edition):

Zhou, T. (2016). Automated Identification of Computer Science Research Papers. (Masters Thesis). University of Windsor. Retrieved from https://scholar.uwindsor.ca/etd/5776

Chicago Manual of Style (16th Edition):

Zhou, Tong. “Automated Identification of Computer Science Research Papers.” 2016. Masters Thesis, University of Windsor. Accessed May 27, 2019. https://scholar.uwindsor.ca/etd/5776.

MLA Handbook (7th Edition):

Zhou, Tong. “Automated Identification of Computer Science Research Papers.” 2016. Web. 27 May 2019.

Vancouver:

Zhou T. Automated Identification of Computer Science Research Papers. [Internet] [Masters thesis]. University of Windsor; 2016. [cited 2019 May 27]. Available from: https://scholar.uwindsor.ca/etd/5776.

Council of Science Editors:

Zhou T. Automated Identification of Computer Science Research Papers. [Masters Thesis]. University of Windsor; 2016. Available from: https://scholar.uwindsor.ca/etd/5776

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