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Dept: Electrical and Computer Engineering

You searched for subject:(Feature selection). Showing records 1 – 10 of 10 total matches.

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Virginia Tech

1. Ha, Sook Shin. Dimensionality Reduction, Feature Selection and Visualization of Biological Data.

Degree: PhD, Electrical and Computer Engineering, 2012, Virginia Tech

 Due to the high dimensionality of most biological data, it is a difficult task to directly analyze, model and visualize the data to gain biological… (more)

Subjects/Keywords: Gene Expression; Feature Selection; Dimensionality Reduction; PPI network; Pathways; Visualization; Weight

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APA (6th Edition):

Ha, S. S. (2012). Dimensionality Reduction, Feature Selection and Visualization of Biological Data. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/77169

Chicago Manual of Style (16th Edition):

Ha, Sook Shin. “Dimensionality Reduction, Feature Selection and Visualization of Biological Data.” 2012. Doctoral Dissertation, Virginia Tech. Accessed June 25, 2019. http://hdl.handle.net/10919/77169.

MLA Handbook (7th Edition):

Ha, Sook Shin. “Dimensionality Reduction, Feature Selection and Visualization of Biological Data.” 2012. Web. 25 Jun 2019.

Vancouver:

Ha SS. Dimensionality Reduction, Feature Selection and Visualization of Biological Data. [Internet] [Doctoral dissertation]. Virginia Tech; 2012. [cited 2019 Jun 25]. Available from: http://hdl.handle.net/10919/77169.

Council of Science Editors:

Ha SS. Dimensionality Reduction, Feature Selection and Visualization of Biological Data. [Doctoral Dissertation]. Virginia Tech; 2012. Available from: http://hdl.handle.net/10919/77169


Georgia Tech

2. Sanders, Teresa H. Multimodal assessment of Parkinson's disease using electrophysiology and automated motor scoring.

Degree: PhD, Electrical and Computer Engineering, 2014, Georgia Tech

 A suite of signal processing algorithms designed for extracting information from brain electrophysiology and movement signals, along with new insights gained by applying these tools… (more)

Subjects/Keywords: Electrophysiology; Parkinson's disease; Multimodal monitoring; Signal processing; Cross-frequency-coupling; Optimal feature selection; Parkinson's disease; Electrophysiology; Signal processing; Motion detectors; Algorithms

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APA (6th Edition):

Sanders, T. H. (2014). Multimodal assessment of Parkinson's disease using electrophysiology and automated motor scoring. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/51970

Chicago Manual of Style (16th Edition):

Sanders, Teresa H. “Multimodal assessment of Parkinson's disease using electrophysiology and automated motor scoring.” 2014. Doctoral Dissertation, Georgia Tech. Accessed June 25, 2019. http://hdl.handle.net/1853/51970.

MLA Handbook (7th Edition):

Sanders, Teresa H. “Multimodal assessment of Parkinson's disease using electrophysiology and automated motor scoring.” 2014. Web. 25 Jun 2019.

Vancouver:

Sanders TH. Multimodal assessment of Parkinson's disease using electrophysiology and automated motor scoring. [Internet] [Doctoral dissertation]. Georgia Tech; 2014. [cited 2019 Jun 25]. Available from: http://hdl.handle.net/1853/51970.

Council of Science Editors:

Sanders TH. Multimodal assessment of Parkinson's disease using electrophysiology and automated motor scoring. [Doctoral Dissertation]. Georgia Tech; 2014. Available from: http://hdl.handle.net/1853/51970


Queens University

3. Taati, Babak. Generation and Optimization of Local Shape Descriptors for Point Matching in 3-D Surfaces .

Degree: Electrical and Computer Engineering, 2009, Queens University

 We formulate Local Shape Descriptor selection for model-based object recognition in range data as an optimization problem and offer a platform that facilitates a solution.… (more)

Subjects/Keywords: Computer vision; Range data; Object recognition; Tracking; Local shape descriptor; Point matching; Pose estimation; Pose acquisition; 3-D; 3D; Point cloud; Satellite tracking; Optimization; Range image processing; Range image; RANSAC; Registration; Alignment; Surface; Computational geometry; Detection; Localization; Model-based; Object identification; Point correspondence; Feature selection; Variable-Dimensional Local Shape Descriptors; VD-LSD; LSD; Genetic algorithm; Simulated annealing; Forward feature selection; Multivariate features; Subset selection; Local properties; LIDAR; Dense stereo; Stereo; Precision; Feature matching; Machine learning; Training; Learning phase; Preprocessing

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APA (6th Edition):

Taati, B. (2009). Generation and Optimization of Local Shape Descriptors for Point Matching in 3-D Surfaces . (Thesis). Queens University. Retrieved from http://hdl.handle.net/1974/5107

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):

Taati, Babak. “Generation and Optimization of Local Shape Descriptors for Point Matching in 3-D Surfaces .” 2009. Thesis, Queens University. Accessed June 25, 2019. http://hdl.handle.net/1974/5107.

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

MLA Handbook (7th Edition):

Taati, Babak. “Generation and Optimization of Local Shape Descriptors for Point Matching in 3-D Surfaces .” 2009. Web. 25 Jun 2019.

Vancouver:

Taati B. Generation and Optimization of Local Shape Descriptors for Point Matching in 3-D Surfaces . [Internet] [Thesis]. Queens University; 2009. [cited 2019 Jun 25]. Available from: http://hdl.handle.net/1974/5107.

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

Council of Science Editors:

Taati B. Generation and Optimization of Local Shape Descriptors for Point Matching in 3-D Surfaces . [Thesis]. Queens University; 2009. Available from: http://hdl.handle.net/1974/5107

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


University of Florida

4. Chandran,Manu. Analysis of Bayesian Group-Lasso in Regression Models.

Degree: MS, Electrical and Computer Engineering, 2011, University of Florida

 The Group-Lasso estimator, used in regression analysis, does not calculate the variance estimates of regression coefficients. Such estimates are important, since they represent the confidence… (more)

Subjects/Keywords: Datasets; Error rates; Group size; Group structure; Linear regression; Machine learning; Mathematical variables; Parametric models; Regression analysis; Statistical discrepancies; bayesian  – feature  – group  – lasso  – selection

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APA (6th Edition):

Chandran,Manu. (2011). Analysis of Bayesian Group-Lasso in Regression Models. (Masters Thesis). University of Florida. Retrieved from http://ufdc.ufl.edu/UFE0043490

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Chicago Manual of Style (16th Edition):

Chandran,Manu. “Analysis of Bayesian Group-Lasso in Regression Models.” 2011. Masters Thesis, University of Florida. Accessed June 25, 2019. http://ufdc.ufl.edu/UFE0043490.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

MLA Handbook (7th Edition):

Chandran,Manu. “Analysis of Bayesian Group-Lasso in Regression Models.” 2011. Web. 25 Jun 2019.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Vancouver:

Chandran,Manu. Analysis of Bayesian Group-Lasso in Regression Models. [Internet] [Masters thesis]. University of Florida; 2011. [cited 2019 Jun 25]. Available from: http://ufdc.ufl.edu/UFE0043490.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Council of Science Editors:

Chandran,Manu. Analysis of Bayesian Group-Lasso in Regression Models. [Masters Thesis]. University of Florida; 2011. Available from: http://ufdc.ufl.edu/UFE0043490

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete


University of New Mexico

5. Ojha, Tushar. Prediction of Graduation Delay Based on Student Characterisitics and Performance.

Degree: Electrical and Computer Engineering, 2017, University of New Mexico

  A college student's success depends on many factors including pre-university characteristics and university student support services. Student graduation rates are often used as an… (more)

Subjects/Keywords: Higer Education; Analytics; Machine Learning; Data Mining; Predicitve Analytics; Feature Selection; Graduation Delay; Graduation Rate; Electrical and Computer Engineering; Other Computer Engineering

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APA (6th Edition):

Ojha, T. (2017). Prediction of Graduation Delay Based on Student Characterisitics and Performance. (Masters Thesis). University of New Mexico. Retrieved from https://digitalrepository.unm.edu/ece_etds/355

Chicago Manual of Style (16th Edition):

Ojha, Tushar. “Prediction of Graduation Delay Based on Student Characterisitics and Performance.” 2017. Masters Thesis, University of New Mexico. Accessed June 25, 2019. https://digitalrepository.unm.edu/ece_etds/355.

MLA Handbook (7th Edition):

Ojha, Tushar. “Prediction of Graduation Delay Based on Student Characterisitics and Performance.” 2017. Web. 25 Jun 2019.

Vancouver:

Ojha T. Prediction of Graduation Delay Based on Student Characterisitics and Performance. [Internet] [Masters thesis]. University of New Mexico; 2017. [cited 2019 Jun 25]. Available from: https://digitalrepository.unm.edu/ece_etds/355.

Council of Science Editors:

Ojha T. Prediction of Graduation Delay Based on Student Characterisitics and Performance. [Masters Thesis]. University of New Mexico; 2017. Available from: https://digitalrepository.unm.edu/ece_etds/355


Georgia Tech

6. Jeon, Woojay. Speech Analysis and Cognition Using Category-Dependent Features in a Model of the Central Auditory System.

Degree: PhD, Electrical and Computer Engineering, 2006, Georgia Tech

 It is well known that machines perform far worse than humans in recognizing speech and audio, especially in noisy environments. One method of addressing this… (more)

Subjects/Keywords: Speech processing; Speech recognition; Feature selection; Pattern recognition; Speech analysis; Auditory model; Auditory cortex; Automatic speech recognition

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APA (6th Edition):

Jeon, W. (2006). Speech Analysis and Cognition Using Category-Dependent Features in a Model of the Central Auditory System. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/14061

Chicago Manual of Style (16th Edition):

Jeon, Woojay. “Speech Analysis and Cognition Using Category-Dependent Features in a Model of the Central Auditory System.” 2006. Doctoral Dissertation, Georgia Tech. Accessed June 25, 2019. http://hdl.handle.net/1853/14061.

MLA Handbook (7th Edition):

Jeon, Woojay. “Speech Analysis and Cognition Using Category-Dependent Features in a Model of the Central Auditory System.” 2006. Web. 25 Jun 2019.

Vancouver:

Jeon W. Speech Analysis and Cognition Using Category-Dependent Features in a Model of the Central Auditory System. [Internet] [Doctoral dissertation]. Georgia Tech; 2006. [cited 2019 Jun 25]. Available from: http://hdl.handle.net/1853/14061.

Council of Science Editors:

Jeon W. Speech Analysis and Cognition Using Category-Dependent Features in a Model of the Central Auditory System. [Doctoral Dissertation]. Georgia Tech; 2006. Available from: http://hdl.handle.net/1853/14061

7. Johnson, Ashley Nzinga. A statistical framework for the analysis of neural control of movement with aging and other clinical applications.

Degree: PhD, Electrical and Computer Engineering, 2012, Georgia Tech

 The majority of daily living tasks necessitate the use of bimanual movements or concurrent cognitive processing, which are often more difficult for elderly adults. With… (more)

Subjects/Keywords: Electroencephalogram; Electromyogram; Classification; Coherence; Feature selection; Aging; Motor neurons; Efferent pathways; Neuromuscular transmission; Cerebrovascular disease—Patients

…and task) . . . . . . . . . 60 Box plot of feature selection algorithms forward… …backward, and branch-and-bound feature selection algorithms. The inclusion and optimization of… …the methodology of data preparation, feature selection, and classification to motor stroke… …Simple Task Parameter Selection (Temporal, Signal) Aging Complex Task Feature… …feature extraction, feature selection, and classification [24]. Feature extraction and… 

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APA (6th Edition):

Johnson, A. N. (2012). A statistical framework for the analysis of neural control of movement with aging and other clinical applications. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/47573

Chicago Manual of Style (16th Edition):

Johnson, Ashley Nzinga. “A statistical framework for the analysis of neural control of movement with aging and other clinical applications.” 2012. Doctoral Dissertation, Georgia Tech. Accessed June 25, 2019. http://hdl.handle.net/1853/47573.

MLA Handbook (7th Edition):

Johnson, Ashley Nzinga. “A statistical framework for the analysis of neural control of movement with aging and other clinical applications.” 2012. Web. 25 Jun 2019.

Vancouver:

Johnson AN. A statistical framework for the analysis of neural control of movement with aging and other clinical applications. [Internet] [Doctoral dissertation]. Georgia Tech; 2012. [cited 2019 Jun 25]. Available from: http://hdl.handle.net/1853/47573.

Council of Science Editors:

Johnson AN. A statistical framework for the analysis of neural control of movement with aging and other clinical applications. [Doctoral Dissertation]. Georgia Tech; 2012. Available from: http://hdl.handle.net/1853/47573

8. Byrne, Evan Michael. Sparse Multinomial Logistic Regression via Approximate Message Passing.

Degree: MS, Electrical and Computer Engineering, 2015, The Ohio State University

 For the problem of multi-class linear classification and feature selection, we propose new approximate message passing algorithms based on Hybrid Generalized Approximate Message Passing (Hybrid-GAMP)… (more)

Subjects/Keywords: Electrical Engineering; multinomial logistic regression; multiclass linear classification; approximate message passing; feature selection

…Linear classification In this thesis we explore linear classification and feature selection… …estimate that subset, which is known as “feature selection.” We can do this by simply examining… …classification and feature selection that display our previously listed attributes. For instance, in… …fMRI MultiVoxel Pattern Analysis (MVPA), feature selection can be used to determine… …feature selection is accomplished by examining the row-support of X is accomplished through… 

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APA (6th Edition):

Byrne, E. M. (2015). Sparse Multinomial Logistic Regression via Approximate Message Passing. (Masters Thesis). The Ohio State University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=osu1437416281

Chicago Manual of Style (16th Edition):

Byrne, Evan Michael. “Sparse Multinomial Logistic Regression via Approximate Message Passing.” 2015. Masters Thesis, The Ohio State University. Accessed June 25, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1437416281.

MLA Handbook (7th Edition):

Byrne, Evan Michael. “Sparse Multinomial Logistic Regression via Approximate Message Passing.” 2015. Web. 25 Jun 2019.

Vancouver:

Byrne EM. Sparse Multinomial Logistic Regression via Approximate Message Passing. [Internet] [Masters thesis]. The Ohio State University; 2015. [cited 2019 Jun 25]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1437416281.

Council of Science Editors:

Byrne EM. Sparse Multinomial Logistic Regression via Approximate Message Passing. [Masters Thesis]. The Ohio State University; 2015. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1437416281

9. Samiappan, Sathishkumar. Spectral band selection for ensemble classification of hyperspectral images with applications to agriculture and food safety.

Degree: PhD, Electrical and Computer Engineering, 2014, Mississippi State University

  In this dissertation, an ensemble non-uniform spectral feature selection and a kernel density decision fusion framework are proposed for the classification of hyperspectral data… (more)

Subjects/Keywords: corn aflatoxin; food safety; image processing; pattern recognition; supervised classification; hyperspectral; feature selection

…2.4 Classical Feature Selection Techniques ...............................................29… …2.4.1 Feature Selection Techniques for SVM .........................................29 2.4.2… …Random Feature selection ..............................................................30 2.5… …36 iv III. NON-UNIFORM RANDOM FEATURE SELECTION, DECISION FUSION BASED ON KERNEL DENSITY… …41 Non-Uniform Random Feature Selection ............................................42… 

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APA (6th Edition):

Samiappan, S. (2014). Spectral band selection for ensemble classification of hyperspectral images with applications to agriculture and food safety. (Doctoral Dissertation). Mississippi State University. Retrieved from http://sun.library.msstate.edu/ETD-db/theses/available/etd-06262014-114816/ ;

Chicago Manual of Style (16th Edition):

Samiappan, Sathishkumar. “Spectral band selection for ensemble classification of hyperspectral images with applications to agriculture and food safety.” 2014. Doctoral Dissertation, Mississippi State University. Accessed June 25, 2019. http://sun.library.msstate.edu/ETD-db/theses/available/etd-06262014-114816/ ;.

MLA Handbook (7th Edition):

Samiappan, Sathishkumar. “Spectral band selection for ensemble classification of hyperspectral images with applications to agriculture and food safety.” 2014. Web. 25 Jun 2019.

Vancouver:

Samiappan S. Spectral band selection for ensemble classification of hyperspectral images with applications to agriculture and food safety. [Internet] [Doctoral dissertation]. Mississippi State University; 2014. [cited 2019 Jun 25]. Available from: http://sun.library.msstate.edu/ETD-db/theses/available/etd-06262014-114816/ ;.

Council of Science Editors:

Samiappan S. Spectral band selection for ensemble classification of hyperspectral images with applications to agriculture and food safety. [Doctoral Dissertation]. Mississippi State University; 2014. Available from: http://sun.library.msstate.edu/ETD-db/theses/available/etd-06262014-114816/ ;

10. Chiu, Leung Kin. Efficient audio signal processing for embedded systems.

Degree: PhD, Electrical and Computer Engineering, 2012, Georgia Tech

 We investigated two design strategies that would allow us to efficiently process audio signals on embedded systems such as mobile phones and portable electronics. In… (more)

Subjects/Keywords: AdaBoost; Programmable analog circuit; Audio feature selection; Analog classifier; Loudspeaker protection; Sound enhancement; Embedded computer systems; Signal processing Digital techniques; Mobile communication systems; Computer sound processing; Piezoelectric devices; Algorithms

…VII DESIGNING ANALOG AUDIO CLASSIFIERS WITH ADABOOST-BASED FEATURE SELECTION… …75 7.2 AdaBoost and Feature Selection . . . . . . . . . . . . . . . . . . . . . . . 77… …employed to store classifier weights. Moreover, we incorporated a feature selection algorithm to… …7.3.1 All-Matlab simulation: Feature Extraction and Classification . . 79 7.3.2 FPAA… …Feature Extraction and Matlab Classification Simulation 81 7.4 Discussion… 

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APA (6th Edition):

Chiu, L. K. (2012). Efficient audio signal processing for embedded systems. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/44775

Chicago Manual of Style (16th Edition):

Chiu, Leung Kin. “Efficient audio signal processing for embedded systems.” 2012. Doctoral Dissertation, Georgia Tech. Accessed June 25, 2019. http://hdl.handle.net/1853/44775.

MLA Handbook (7th Edition):

Chiu, Leung Kin. “Efficient audio signal processing for embedded systems.” 2012. Web. 25 Jun 2019.

Vancouver:

Chiu LK. Efficient audio signal processing for embedded systems. [Internet] [Doctoral dissertation]. Georgia Tech; 2012. [cited 2019 Jun 25]. Available from: http://hdl.handle.net/1853/44775.

Council of Science Editors:

Chiu LK. Efficient audio signal processing for embedded systems. [Doctoral Dissertation]. Georgia Tech; 2012. Available from: http://hdl.handle.net/1853/44775

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