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You searched for subject:(Feature selection). Showing records 1 – 30 of 204 total matches.

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UCLA

1. 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 June 16, 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. 16 Jun 2019.

Vancouver:

Chang K. Complementarity In Data Mining. [Internet] [Thesis]. UCLA; 2015. [cited 2019 Jun 16]. 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 Illinois – Chicago

2. 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 June 16, 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. 16 Jun 2019.

Vancouver:

Wei X. Unsupervised Feature Selection for Heterogeneous Data. [Internet] [Thesis]. University of Illinois – Chicago; 2017. [cited 2019 Jun 16]. 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

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

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

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

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

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APA (6th Edition):

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

Chicago Manual of Style (16th Edition):

Zhang, Yi. “Application of Hyper-geometric Hypothesis-based Quantication and Markov Blanket Feature Selection Methods to Generate Signals for Adverse Drug Reaction Detection.” 2012. Masters Thesis, University of Cincinnati. Accessed June 16, 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. 16 Jun 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 Jun 16]. 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 Southern California

4. 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 June 16, 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. 16 Jun 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 Jun 16]. 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

5. 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 June 16, 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. 16 Jun 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 Jun 16]. 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 Houston

6. 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 June 16, 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. 16 Jun 2019.

Vancouver:

Xu Y. Unsupervised Discovery and Representation of Subspace Trends in Massive Biomedical Datasets. [Internet] [Thesis]. University of Houston; 2015. [cited 2019 Jun 16]. 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

7. 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 June 16, 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. 16 Jun 2019.

Vancouver:

McClanahan BD. Location Inference of Social Media Posts at Hyper-Local Scale. [Internet] [Masters thesis]. University of Connecticut; 2016. [cited 2019 Jun 16]. 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

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

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

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

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

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APA (6th Edition):

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

Chicago Manual of Style (16th Edition):

Yankee, Tara N. “Rank Aggregation of Feature Scoring Methods for Unsupervised Learning.” 2017. Masters Thesis, University of Connecticut. Accessed June 16, 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. 16 Jun 2019.

Vancouver:

Yankee TN. Rank Aggregation of Feature Scoring Methods for Unsupervised Learning. [Internet] [Masters thesis]. University of Connecticut; 2017. [cited 2019 Jun 16]. 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

9. 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 June 16, 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. 16 Jun 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 Jun 16]. 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

10. 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 June 16, 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. 16 Jun 2019.

Vancouver:

Cho SH. Block-based image steganalysis: algorithm and performance evaluation. [Internet] [Doctoral dissertation]. University of Southern California; 2012. [cited 2019 Jun 16]. 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


Case Western Reserve University

11. 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 June 16, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=case1440642294.

MLA Handbook (7th Edition):

Latham, Andrew C. “Multiple-Instance Feature Ranking.” 2016. Web. 16 Jun 2019.

Vancouver:

Latham AC. Multiple-Instance Feature Ranking. [Internet] [Masters thesis]. Case Western Reserve University; 2016. [cited 2019 Jun 16]. 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

12. 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 June 16, 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. 16 Jun 2019.

Vancouver:

Yamak D. Characterization of Coronary Atherosclerotic Plaques by Dual Energy Computed Tomography. [Internet] [Doctoral dissertation]. Arizona State University; 2013. [cited 2019 Jun 16]. 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


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 June 16, 2019. http://arks.princeton.edu/ark:/88435/dsp01hq37vq979.

MLA Handbook (7th Edition):

Wang, Yun. “Feature Screening for the Lasso .” 2015. Web. 16 Jun 2019.

Vancouver:

Wang Y. Feature Screening for the Lasso . [Internet] [Doctoral dissertation]. Princeton University; 2015. [cited 2019 Jun 16]. 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 Cincinnati

14. 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 (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 June 16, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1522420335154157.

MLA Handbook (7th Edition):

Guo, Xinyu. “Improved Feature-Selection for Classification Problems using Multiple Auto-Encoders.” 2018. Web. 16 Jun 2019.

Vancouver:

Guo X. Improved Feature-Selection for Classification Problems using Multiple Auto-Encoders. [Internet] [Doctoral dissertation]. University of Cincinnati; 2018. [cited 2019 Jun 16]. 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


Arizona State University

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

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

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

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

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APA (6th Edition):

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

Chicago Manual of Style (16th Edition):

Davila, Saylisse. “Public Health Surveillance in High-Dimensions with Supervised Learning.” 2010. Doctoral Dissertation, Arizona State University. Accessed June 16, 2019. http://repository.asu.edu/items/8738.

MLA Handbook (7th Edition):

Davila, Saylisse. “Public Health Surveillance in High-Dimensions with Supervised Learning.” 2010. Web. 16 Jun 2019.

Vancouver:

Davila S. Public Health Surveillance in High-Dimensions with Supervised Learning. [Internet] [Doctoral dissertation]. Arizona State University; 2010. [cited 2019 Jun 16]. Available from: http://repository.asu.edu/items/8738.

Council of Science Editors:

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


University of Southern California

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

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

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

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

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APA (6th Edition):

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

Chicago Manual of Style (16th Edition):

Ren, Jie. “Robust feature selection with penalized regression in imbalanced high dimensional data.” 2014. Doctoral Dissertation, University of Southern California. Accessed June 16, 2019. http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/443080/rec/5613.

MLA Handbook (7th Edition):

Ren, Jie. “Robust feature selection with penalized regression in imbalanced high dimensional data.” 2014. Web. 16 Jun 2019.

Vancouver:

Ren J. Robust feature selection with penalized regression in imbalanced high dimensional data. [Internet] [Doctoral dissertation]. University of Southern California; 2014. [cited 2019 Jun 16]. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/443080/rec/5613.

Council of Science Editors:

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


University of Missouri – Columbia

17. Qi, Qi. Statistical model-based methods for observation selection in wireless sensor networks and for feature selection in classification.

Degree: 2012, University of Missouri – Columbia

 We apply statistical model-based approaches to address temporal and spatial observation selection challenges in wireless sensor networks. For temporal observation selection, we present an improved… (more)

Subjects/Keywords: statistical models; feature selection; wireless sensor network; network optimization; observation selection

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

APA (6th Edition):

Qi, Q. (2012). Statistical model-based methods for observation selection in wireless sensor networks and for feature selection in classification. (Thesis). University of Missouri – Columbia. Retrieved from http://hdl.handle.net/10355/15111

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):

Qi, Qi. “Statistical model-based methods for observation selection in wireless sensor networks and for feature selection in classification.” 2012. Thesis, University of Missouri – Columbia. Accessed June 16, 2019. http://hdl.handle.net/10355/15111.

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

MLA Handbook (7th Edition):

Qi, Qi. “Statistical model-based methods for observation selection in wireless sensor networks and for feature selection in classification.” 2012. Web. 16 Jun 2019.

Vancouver:

Qi Q. Statistical model-based methods for observation selection in wireless sensor networks and for feature selection in classification. [Internet] [Thesis]. University of Missouri – Columbia; 2012. [cited 2019 Jun 16]. Available from: http://hdl.handle.net/10355/15111.

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

Council of Science Editors:

Qi Q. Statistical model-based methods for observation selection in wireless sensor networks and for feature selection in classification. [Thesis]. University of Missouri – Columbia; 2012. Available from: http://hdl.handle.net/10355/15111

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


Texas A&M University

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

Degree: 2009, Texas A&M University

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

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

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APA (6th Edition):

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

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

Chicago Manual of Style (16th Edition):

Gopal, Kreshna. “Efficient case-based reasoning through feature weighting, and its application in protein crystallography.” 2009. Thesis, Texas A&M University. Accessed June 16, 2019. http://hdl.handle.net/1969.1/ETD-TAMU-1906.

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

MLA Handbook (7th Edition):

Gopal, Kreshna. “Efficient case-based reasoning through feature weighting, and its application in protein crystallography.” 2009. Web. 16 Jun 2019.

Vancouver:

Gopal K. Efficient case-based reasoning through feature weighting, and its application in protein crystallography. [Internet] [Thesis]. Texas A&M University; 2009. [cited 2019 Jun 16]. Available from: http://hdl.handle.net/1969.1/ETD-TAMU-1906.

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

Council of Science Editors:

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

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


Georgia Tech

19. Hwang, Sungkun. Predicting reliability in multidisciplinary engineering systems under uncertainty.

Degree: MS, Mechanical Engineering, 2016, Georgia Tech

 The proposed study develops a framework that can accurately capture and model input and output variables for multidisciplinary systems to mitigate the computational cost when… (more)

Subjects/Keywords: Stretchable electronics; Dimension reduction; Feature extraction; Feature selection; Artificial neural network; Probabilistic neural network

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APA (6th Edition):

Hwang, S. (2016). Predicting reliability in multidisciplinary engineering systems under uncertainty. (Masters Thesis). Georgia Tech. Retrieved from http://hdl.handle.net/1853/54955

Chicago Manual of Style (16th Edition):

Hwang, Sungkun. “Predicting reliability in multidisciplinary engineering systems under uncertainty.” 2016. Masters Thesis, Georgia Tech. Accessed June 16, 2019. http://hdl.handle.net/1853/54955.

MLA Handbook (7th Edition):

Hwang, Sungkun. “Predicting reliability in multidisciplinary engineering systems under uncertainty.” 2016. Web. 16 Jun 2019.

Vancouver:

Hwang S. Predicting reliability in multidisciplinary engineering systems under uncertainty. [Internet] [Masters thesis]. Georgia Tech; 2016. [cited 2019 Jun 16]. Available from: http://hdl.handle.net/1853/54955.

Council of Science Editors:

Hwang S. Predicting reliability in multidisciplinary engineering systems under uncertainty. [Masters Thesis]. Georgia Tech; 2016. Available from: http://hdl.handle.net/1853/54955


Clemson University

20. Wilson, Matthew Robert. Comparison of Karnin Sensitivity and Principal Component Analysis in Reducing Input Dimensionality.

Degree: MS, Computer Engineering, 2016, Clemson University

 Reducing the input dimensionality of large datasets for subsequent processing will allow the process to become less computationally complex and expensive. This thesis tests if… (more)

Subjects/Keywords: Dimension reduction; feature reduction; feature selection; input reduction; Karnin Sensitivity; Principal Component Analysis

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

APA (6th Edition):

Wilson, M. R. (2016). Comparison of Karnin Sensitivity and Principal Component Analysis in Reducing Input Dimensionality. (Masters Thesis). Clemson University. Retrieved from https://tigerprints.clemson.edu/all_theses/2357

Chicago Manual of Style (16th Edition):

Wilson, Matthew Robert. “Comparison of Karnin Sensitivity and Principal Component Analysis in Reducing Input Dimensionality.” 2016. Masters Thesis, Clemson University. Accessed June 16, 2019. https://tigerprints.clemson.edu/all_theses/2357.

MLA Handbook (7th Edition):

Wilson, Matthew Robert. “Comparison of Karnin Sensitivity and Principal Component Analysis in Reducing Input Dimensionality.” 2016. Web. 16 Jun 2019.

Vancouver:

Wilson MR. Comparison of Karnin Sensitivity and Principal Component Analysis in Reducing Input Dimensionality. [Internet] [Masters thesis]. Clemson University; 2016. [cited 2019 Jun 16]. Available from: https://tigerprints.clemson.edu/all_theses/2357.

Council of Science Editors:

Wilson MR. Comparison of Karnin Sensitivity and Principal Component Analysis in Reducing Input Dimensionality. [Masters Thesis]. Clemson University; 2016. Available from: https://tigerprints.clemson.edu/all_theses/2357


University of Arizona

21. Li, Jiexun. Feature Construction, Selection And Consolidation For Knowledge Discovery .

Degree: 2007, University of Arizona

 With the rapid advance of information technologies, human beings increasingly rely on computers to accumulate, process, and make use of data. Knowledge discovery techniques have… (more)

Subjects/Keywords: knowledge discovery; feature construction; feature selection; feature consolidation

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APA (6th Edition):

Li, J. (2007). Feature Construction, Selection And Consolidation For Knowledge Discovery . (Doctoral Dissertation). University of Arizona. Retrieved from http://hdl.handle.net/10150/193819

Chicago Manual of Style (16th Edition):

Li, Jiexun. “Feature Construction, Selection And Consolidation For Knowledge Discovery .” 2007. Doctoral Dissertation, University of Arizona. Accessed June 16, 2019. http://hdl.handle.net/10150/193819.

MLA Handbook (7th Edition):

Li, Jiexun. “Feature Construction, Selection And Consolidation For Knowledge Discovery .” 2007. Web. 16 Jun 2019.

Vancouver:

Li J. Feature Construction, Selection And Consolidation For Knowledge Discovery . [Internet] [Doctoral dissertation]. University of Arizona; 2007. [cited 2019 Jun 16]. Available from: http://hdl.handle.net/10150/193819.

Council of Science Editors:

Li J. Feature Construction, Selection And Consolidation For Knowledge Discovery . [Doctoral Dissertation]. University of Arizona; 2007. Available from: http://hdl.handle.net/10150/193819


Kennesaw State University

22. Cepeda Mora, Carlos A. Feature Selection and Improving Classification Performance for Malware Detection.

Degree: MSCS, Computer Science, 2017, Kennesaw State University

  The ubiquitous advance of technology has been conducive to the proliferation of cyber threats, resulting in attacks that have grown exponentially. Consequently, researchers have… (more)

Subjects/Keywords: Cyber Security; Machine Learning; Feature selection; Malware detection; Other Computer Engineering

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APA (6th Edition):

Cepeda Mora, C. A. (2017). Feature Selection and Improving Classification Performance for Malware Detection. (Thesis). Kennesaw State University. Retrieved from https://digitalcommons.kennesaw.edu/cs_etd/10

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):

Cepeda Mora, Carlos A. “Feature Selection and Improving Classification Performance for Malware Detection.” 2017. Thesis, Kennesaw State University. Accessed June 16, 2019. https://digitalcommons.kennesaw.edu/cs_etd/10.

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

MLA Handbook (7th Edition):

Cepeda Mora, Carlos A. “Feature Selection and Improving Classification Performance for Malware Detection.” 2017. Web. 16 Jun 2019.

Vancouver:

Cepeda Mora CA. Feature Selection and Improving Classification Performance for Malware Detection. [Internet] [Thesis]. Kennesaw State University; 2017. [cited 2019 Jun 16]. Available from: https://digitalcommons.kennesaw.edu/cs_etd/10.

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

Council of Science Editors:

Cepeda Mora CA. Feature Selection and Improving Classification Performance for Malware Detection. [Thesis]. Kennesaw State University; 2017. Available from: https://digitalcommons.kennesaw.edu/cs_etd/10

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


Texas A&M University

23. Narayana, Sushirdeep. Affect Recognition Using Electroencephalography Features.

Degree: 2017, Texas A&M University

 Affect is the psychological display of emotion often described with three principal dimensions: 1) valence 2) arousal and 3) dominance. This thesis work explores the… (more)

Subjects/Keywords: affect; electroencephalography; machine learning; Support Vector Machines; feature selection

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APA (6th Edition):

Narayana, S. (2017). Affect Recognition Using Electroencephalography Features. (Thesis). Texas A&M University. Retrieved from http://hdl.handle.net/1969.1/161603

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):

Narayana, Sushirdeep. “Affect Recognition Using Electroencephalography Features.” 2017. Thesis, Texas A&M University. Accessed June 16, 2019. http://hdl.handle.net/1969.1/161603.

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

MLA Handbook (7th Edition):

Narayana, Sushirdeep. “Affect Recognition Using Electroencephalography Features.” 2017. Web. 16 Jun 2019.

Vancouver:

Narayana S. Affect Recognition Using Electroencephalography Features. [Internet] [Thesis]. Texas A&M University; 2017. [cited 2019 Jun 16]. Available from: http://hdl.handle.net/1969.1/161603.

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

Council of Science Editors:

Narayana S. Affect Recognition Using Electroencephalography Features. [Thesis]. Texas A&M University; 2017. Available from: http://hdl.handle.net/1969.1/161603

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


University of Minnesota

24. Shrestha, Rini. Estimation of vehicle lateral position using the optical flow method.

Degree: 2013, University of Minnesota

University of Minnesota M.S. thesis. May 2013. Major:Electrical Engineering. Advisor: Dr.Jiann-Shiou Yang. 1 computer file (PDF); vi, 62 pages, appendix, p. 58-62.

With the increasing… (more)

Subjects/Keywords: Feature selection; Heading angle; Homography; Image processing; Optical flow

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

APA (6th Edition):

Shrestha, R. (2013). Estimation of vehicle lateral position using the optical flow method. (Masters Thesis). University of Minnesota. Retrieved from http://purl.umn.edu/157207

Chicago Manual of Style (16th Edition):

Shrestha, Rini. “Estimation of vehicle lateral position using the optical flow method.” 2013. Masters Thesis, University of Minnesota. Accessed June 16, 2019. http://purl.umn.edu/157207.

MLA Handbook (7th Edition):

Shrestha, Rini. “Estimation of vehicle lateral position using the optical flow method.” 2013. Web. 16 Jun 2019.

Vancouver:

Shrestha R. Estimation of vehicle lateral position using the optical flow method. [Internet] [Masters thesis]. University of Minnesota; 2013. [cited 2019 Jun 16]. Available from: http://purl.umn.edu/157207.

Council of Science Editors:

Shrestha R. Estimation of vehicle lateral position using the optical flow method. [Masters Thesis]. University of Minnesota; 2013. Available from: http://purl.umn.edu/157207


Northeastern University

25. Guan, Yue. Bayesian models for unsupervised feature selection.

Degree: PhD, Department of Computer Engineering, 2012, Northeastern University

 This dissertation focuses on developing probabilistic models for unsupervised feature selection. High-dimensional data often contain irrelevant and redundant features, which can hurt learning algorithms. One… (more)

Subjects/Keywords: Bayesian Model; clustering; Feature selection; PCA; unsupervised learning; Computer Engineering

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

APA (6th Edition):

Guan, Y. (2012). Bayesian models for unsupervised feature selection. (Doctoral Dissertation). Northeastern University. Retrieved from http://hdl.handle.net/2047/d20002991

Chicago Manual of Style (16th Edition):

Guan, Yue. “Bayesian models for unsupervised feature selection.” 2012. Doctoral Dissertation, Northeastern University. Accessed June 16, 2019. http://hdl.handle.net/2047/d20002991.

MLA Handbook (7th Edition):

Guan, Yue. “Bayesian models for unsupervised feature selection.” 2012. Web. 16 Jun 2019.

Vancouver:

Guan Y. Bayesian models for unsupervised feature selection. [Internet] [Doctoral dissertation]. Northeastern University; 2012. [cited 2019 Jun 16]. Available from: http://hdl.handle.net/2047/d20002991.

Council of Science Editors:

Guan Y. Bayesian models for unsupervised feature selection. [Doctoral Dissertation]. Northeastern University; 2012. Available from: http://hdl.handle.net/2047/d20002991


Case Western Reserve University

26. Gao, Cen. Research in target specificity based on microRNA-target interaction data.

Degree: MSs, EECS - Computer and Information Sciences, 2010, Case Western Reserve University

  MicroRNAs regulate their target mRNAs before they are translated into proteins. Although it has been demonstrated that the regulation is through partial binding of… (more)

Subjects/Keywords: MicroRNA; MICRORNA-TARGET; SEED SITE; feature selection; MRNA

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APA (6th Edition):

Gao, C. (2010). Research in target specificity based on microRNA-target interaction data. (Masters Thesis). Case Western Reserve University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=case1275685130

Chicago Manual of Style (16th Edition):

Gao, Cen. “Research in target specificity based on microRNA-target interaction data.” 2010. Masters Thesis, Case Western Reserve University. Accessed June 16, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=case1275685130.

MLA Handbook (7th Edition):

Gao, Cen. “Research in target specificity based on microRNA-target interaction data.” 2010. Web. 16 Jun 2019.

Vancouver:

Gao C. Research in target specificity based on microRNA-target interaction data. [Internet] [Masters thesis]. Case Western Reserve University; 2010. [cited 2019 Jun 16]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=case1275685130.

Council of Science Editors:

Gao C. Research in target specificity based on microRNA-target interaction data. [Masters Thesis]. Case Western Reserve University; 2010. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=case1275685130


Penn State University

27. D'orazio, Vito James. International Military Cooperation: From Concepts to Constructs.

Degree: PhD, Political Science, 2013, Penn State University

 International cooperation on issues of security is a central concept in many theoretical debates in international relations. This dissertation is an attempt to lay the… (more)

Subjects/Keywords: international relations; measurement; military cooperation; foreign policy; alliances; feature selection

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APA (6th Edition):

D'orazio, V. J. (2013). International Military Cooperation: From Concepts to Constructs. (Doctoral Dissertation). Penn State University. Retrieved from https://etda.libraries.psu.edu/catalog/19845

Chicago Manual of Style (16th Edition):

D'orazio, Vito James. “International Military Cooperation: From Concepts to Constructs.” 2013. Doctoral Dissertation, Penn State University. Accessed June 16, 2019. https://etda.libraries.psu.edu/catalog/19845.

MLA Handbook (7th Edition):

D'orazio, Vito James. “International Military Cooperation: From Concepts to Constructs.” 2013. Web. 16 Jun 2019.

Vancouver:

D'orazio VJ. International Military Cooperation: From Concepts to Constructs. [Internet] [Doctoral dissertation]. Penn State University; 2013. [cited 2019 Jun 16]. Available from: https://etda.libraries.psu.edu/catalog/19845.

Council of Science Editors:

D'orazio VJ. International Military Cooperation: From Concepts to Constructs. [Doctoral Dissertation]. Penn State University; 2013. Available from: https://etda.libraries.psu.edu/catalog/19845


Penn State University

28. Zhong, Wei. feature screening and variable selection for ultrahigh dimensional data analysis.

Degree: PhD, Statistics, 2012, Penn State University

 This dissertation is concerned with feature screening and variable selection in ultrahigh dimensional data analysis, where the number of predictors, p, greatly exceeds the sample… (more)

Subjects/Keywords: ultrahigh dimensionality; distance correlation; feature screening; sure screening property; variable selection

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

APA (6th Edition):

Zhong, W. (2012). feature screening and variable selection for ultrahigh dimensional data analysis. (Doctoral Dissertation). Penn State University. Retrieved from https://etda.libraries.psu.edu/catalog/14917

Chicago Manual of Style (16th Edition):

Zhong, Wei. “feature screening and variable selection for ultrahigh dimensional data analysis.” 2012. Doctoral Dissertation, Penn State University. Accessed June 16, 2019. https://etda.libraries.psu.edu/catalog/14917.

MLA Handbook (7th Edition):

Zhong, Wei. “feature screening and variable selection for ultrahigh dimensional data analysis.” 2012. Web. 16 Jun 2019.

Vancouver:

Zhong W. feature screening and variable selection for ultrahigh dimensional data analysis. [Internet] [Doctoral dissertation]. Penn State University; 2012. [cited 2019 Jun 16]. Available from: https://etda.libraries.psu.edu/catalog/14917.

Council of Science Editors:

Zhong W. feature screening and variable selection for ultrahigh dimensional data analysis. [Doctoral Dissertation]. Penn State University; 2012. Available from: https://etda.libraries.psu.edu/catalog/14917


University of Georgia

29. Vahid, Sara. Feature selection in medical domains.

Degree: MS, Computer Science, 2017, University of Georgia

 Usually datasets in the medical domain contain so many features while not all these features are useful for training the classifier. This research aims to… (more)

Subjects/Keywords: Feature Selection; Machine Learning; Classification Algorithm; Medical Domain

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

APA (6th Edition):

Vahid, S. (2017). Feature selection in medical domains. (Masters Thesis). University of Georgia. Retrieved from http://hdl.handle.net/10724/37204

Chicago Manual of Style (16th Edition):

Vahid, Sara. “Feature selection in medical domains.” 2017. Masters Thesis, University of Georgia. Accessed June 16, 2019. http://hdl.handle.net/10724/37204.

MLA Handbook (7th Edition):

Vahid, Sara. “Feature selection in medical domains.” 2017. Web. 16 Jun 2019.

Vancouver:

Vahid S. Feature selection in medical domains. [Internet] [Masters thesis]. University of Georgia; 2017. [cited 2019 Jun 16]. Available from: http://hdl.handle.net/10724/37204.

Council of Science Editors:

Vahid S. Feature selection in medical domains. [Masters Thesis]. University of Georgia; 2017. Available from: http://hdl.handle.net/10724/37204


University of Georgia

30. Vahid, Sara. Feature selection in medical domains.

Degree: MS, Computer Science, 2017, University of Georgia

 Usually datasets in the medical domain contain so many features while not all these features are useful for training the classifier. This research aims to… (more)

Subjects/Keywords: Feature Selection; Machine Learning; Classification Algorithm; Medical Domain

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

APA (6th Edition):

Vahid, S. (2017). Feature selection in medical domains. (Masters Thesis). University of Georgia. Retrieved from http://hdl.handle.net/10724/37047

Chicago Manual of Style (16th Edition):

Vahid, Sara. “Feature selection in medical domains.” 2017. Masters Thesis, University of Georgia. Accessed June 16, 2019. http://hdl.handle.net/10724/37047.

MLA Handbook (7th Edition):

Vahid, Sara. “Feature selection in medical domains.” 2017. Web. 16 Jun 2019.

Vancouver:

Vahid S. Feature selection in medical domains. [Internet] [Masters thesis]. University of Georgia; 2017. [cited 2019 Jun 16]. Available from: http://hdl.handle.net/10724/37047.

Council of Science Editors:

Vahid S. Feature selection in medical domains. [Masters Thesis]. University of Georgia; 2017. Available from: http://hdl.handle.net/10724/37047

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