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You searched for subject:(Support Vector Machines). Showing records 1 – 30 of 510 total matches.

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Virginia Commonwealth University

1. Hess, Eric. Ramp Loss SVM with L1-Norm Regularizaion.

Degree: MS, Mathematical Sciences, 2014, Virginia Commonwealth University

  The Support Vector Machine (SVM) classification method has recently gained popularity due to the ease of implementing non-linear separating surfaces. SVM is an optimization… (more)

Subjects/Keywords: Classification; Support Vector Machines; Analysis

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APA (6th Edition):

Hess, E. (2014). Ramp Loss SVM with L1-Norm Regularizaion. (Thesis). Virginia Commonwealth University. Retrieved from https://doi.org/10.25772/X3KY-Q384 ; https://scholarscompass.vcu.edu/etd/3538

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):

Hess, Eric. “Ramp Loss SVM with L1-Norm Regularizaion.” 2014. Thesis, Virginia Commonwealth University. Accessed August 07, 2020. https://doi.org/10.25772/X3KY-Q384 ; https://scholarscompass.vcu.edu/etd/3538.

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

MLA Handbook (7th Edition):

Hess, Eric. “Ramp Loss SVM with L1-Norm Regularizaion.” 2014. Web. 07 Aug 2020.

Vancouver:

Hess E. Ramp Loss SVM with L1-Norm Regularizaion. [Internet] [Thesis]. Virginia Commonwealth University; 2014. [cited 2020 Aug 07]. Available from: https://doi.org/10.25772/X3KY-Q384 ; https://scholarscompass.vcu.edu/etd/3538.

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

Council of Science Editors:

Hess E. Ramp Loss SVM with L1-Norm Regularizaion. [Thesis]. Virginia Commonwealth University; 2014. Available from: https://doi.org/10.25772/X3KY-Q384 ; https://scholarscompass.vcu.edu/etd/3538

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


Colorado State University

2. Carr, Brittany M. Generic support vector machines and Radon's theorem.

Degree: MS(M.S.), Mathematics, 2019, Colorado State University

 A support vector machine, (SVM), is an algorithm which finds a hyperplane that optimally separates labeled data points in Rn into positive and negative classes.… (more)

Subjects/Keywords: support vector machines; Radon's theorem

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APA (6th Edition):

Carr, B. M. (2019). Generic support vector machines and Radon's theorem. (Masters Thesis). Colorado State University. Retrieved from http://hdl.handle.net/10217/197262

Chicago Manual of Style (16th Edition):

Carr, Brittany M. “Generic support vector machines and Radon's theorem.” 2019. Masters Thesis, Colorado State University. Accessed August 07, 2020. http://hdl.handle.net/10217/197262.

MLA Handbook (7th Edition):

Carr, Brittany M. “Generic support vector machines and Radon's theorem.” 2019. Web. 07 Aug 2020.

Vancouver:

Carr BM. Generic support vector machines and Radon's theorem. [Internet] [Masters thesis]. Colorado State University; 2019. [cited 2020 Aug 07]. Available from: http://hdl.handle.net/10217/197262.

Council of Science Editors:

Carr BM. Generic support vector machines and Radon's theorem. [Masters Thesis]. Colorado State University; 2019. Available from: http://hdl.handle.net/10217/197262


Université de Sherbrooke

3. D'Orangeville, Vincent. Analyse automatique de données par Support Vector Machines non supervisés.

Degree: 2012, Université de Sherbrooke

 Cette dissertation présente un ensemble d'algorithmes visant à en permettre un usage rapide, robuste et automatique des « Support Vector Machines » (SVM) non supervisés… (more)

Subjects/Keywords: Support Vector Machines; Support Vector Clustering; Support Vector Domain Description

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APA (6th Edition):

D'Orangeville, V. (2012). Analyse automatique de données par Support Vector Machines non supervisés. (Doctoral Dissertation). Université de Sherbrooke. Retrieved from http://hdl.handle.net/11143/6678

Chicago Manual of Style (16th Edition):

D'Orangeville, Vincent. “Analyse automatique de données par Support Vector Machines non supervisés.” 2012. Doctoral Dissertation, Université de Sherbrooke. Accessed August 07, 2020. http://hdl.handle.net/11143/6678.

MLA Handbook (7th Edition):

D'Orangeville, Vincent. “Analyse automatique de données par Support Vector Machines non supervisés.” 2012. Web. 07 Aug 2020.

Vancouver:

D'Orangeville V. Analyse automatique de données par Support Vector Machines non supervisés. [Internet] [Doctoral dissertation]. Université de Sherbrooke; 2012. [cited 2020 Aug 07]. Available from: http://hdl.handle.net/11143/6678.

Council of Science Editors:

D'Orangeville V. Analyse automatique de données par Support Vector Machines non supervisés. [Doctoral Dissertation]. Université de Sherbrooke; 2012. Available from: http://hdl.handle.net/11143/6678


University of Alberta

4. Miao, Chuxiong. A support vector machine model for pipe crack size classification.

Degree: MS, Department of Mechanical Engineering, 2009, University of Alberta

 Classifying pipe cracks by size from their pulse-echo ultrasonic signal is difficult but highly significant for the defect evaluation required in pipe testing and maintenance… (more)

Subjects/Keywords: KFD; support vector machines; data dependent kernel

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APA (6th Edition):

Miao, C. (2009). A support vector machine model for pipe crack size classification. (Masters Thesis). University of Alberta. Retrieved from https://era.library.ualberta.ca/files/sb397995n

Chicago Manual of Style (16th Edition):

Miao, Chuxiong. “A support vector machine model for pipe crack size classification.” 2009. Masters Thesis, University of Alberta. Accessed August 07, 2020. https://era.library.ualberta.ca/files/sb397995n.

MLA Handbook (7th Edition):

Miao, Chuxiong. “A support vector machine model for pipe crack size classification.” 2009. Web. 07 Aug 2020.

Vancouver:

Miao C. A support vector machine model for pipe crack size classification. [Internet] [Masters thesis]. University of Alberta; 2009. [cited 2020 Aug 07]. Available from: https://era.library.ualberta.ca/files/sb397995n.

Council of Science Editors:

Miao C. A support vector machine model for pipe crack size classification. [Masters Thesis]. University of Alberta; 2009. Available from: https://era.library.ualberta.ca/files/sb397995n


Cornell University

5. Yu, Chun Nam. Improved Learning Of Structural Support Vector Machines: Training With Latent Variables And Nonlinear Kernels .

Degree: 2011, Cornell University

 Structured output prediction in machine learning is the study of learning to predict complex objects consisting of many correlated parts, such as sequences, trees, or… (more)

Subjects/Keywords: Structured Output Learning; Support Vector Machines; Kernels

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APA (6th Edition):

Yu, C. N. (2011). Improved Learning Of Structural Support Vector Machines: Training With Latent Variables And Nonlinear Kernels . (Thesis). Cornell University. Retrieved from http://hdl.handle.net/1813/33469

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):

Yu, Chun Nam. “Improved Learning Of Structural Support Vector Machines: Training With Latent Variables And Nonlinear Kernels .” 2011. Thesis, Cornell University. Accessed August 07, 2020. http://hdl.handle.net/1813/33469.

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

MLA Handbook (7th Edition):

Yu, Chun Nam. “Improved Learning Of Structural Support Vector Machines: Training With Latent Variables And Nonlinear Kernels .” 2011. Web. 07 Aug 2020.

Vancouver:

Yu CN. Improved Learning Of Structural Support Vector Machines: Training With Latent Variables And Nonlinear Kernels . [Internet] [Thesis]. Cornell University; 2011. [cited 2020 Aug 07]. Available from: http://hdl.handle.net/1813/33469.

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

Council of Science Editors:

Yu CN. Improved Learning Of Structural Support Vector Machines: Training With Latent Variables And Nonlinear Kernels . [Thesis]. Cornell University; 2011. Available from: http://hdl.handle.net/1813/33469

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


University of North Texas

6. Giritharan, Balathasan. Incremental Learning with Large Datasets.

Degree: 2012, University of North Texas

 This dissertation focuses on the novel learning strategy based on geometric support vector machines to address the difficulties of processing immense data set. Support vector(more)

Subjects/Keywords: Support vector machines; incremental learning; large datasets

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APA (6th Edition):

Giritharan, B. (2012). Incremental Learning with Large Datasets. (Thesis). University of North Texas. Retrieved from https://digital.library.unt.edu/ark:/67531/metadc149595/

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):

Giritharan, Balathasan. “Incremental Learning with Large Datasets.” 2012. Thesis, University of North Texas. Accessed August 07, 2020. https://digital.library.unt.edu/ark:/67531/metadc149595/.

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

MLA Handbook (7th Edition):

Giritharan, Balathasan. “Incremental Learning with Large Datasets.” 2012. Web. 07 Aug 2020.

Vancouver:

Giritharan B. Incremental Learning with Large Datasets. [Internet] [Thesis]. University of North Texas; 2012. [cited 2020 Aug 07]. Available from: https://digital.library.unt.edu/ark:/67531/metadc149595/.

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

Council of Science Editors:

Giritharan B. Incremental Learning with Large Datasets. [Thesis]. University of North Texas; 2012. Available from: https://digital.library.unt.edu/ark:/67531/metadc149595/

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


Delft University of Technology

7. Liscio, Enrico (author). Learning Autonomous Grasping Strategies for a Care Robot: A Machine Learning approach.

Degree: 2017, Delft University of Technology

Autonomous grasping is a key requisite for the autonomy of robots. However, grasping of unknown objects in domestic environments is difficult due to the presence… (more)

Subjects/Keywords: Machine Learning; Robotics; Grasping; Support Vector Machines

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APA (6th Edition):

Liscio, E. (. (2017). Learning Autonomous Grasping Strategies for a Care Robot: A Machine Learning approach. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:261fb6d5-bcda-471a-907a-e3fdcec20e50

Chicago Manual of Style (16th Edition):

Liscio, Enrico (author). “Learning Autonomous Grasping Strategies for a Care Robot: A Machine Learning approach.” 2017. Masters Thesis, Delft University of Technology. Accessed August 07, 2020. http://resolver.tudelft.nl/uuid:261fb6d5-bcda-471a-907a-e3fdcec20e50.

MLA Handbook (7th Edition):

Liscio, Enrico (author). “Learning Autonomous Grasping Strategies for a Care Robot: A Machine Learning approach.” 2017. Web. 07 Aug 2020.

Vancouver:

Liscio E(. Learning Autonomous Grasping Strategies for a Care Robot: A Machine Learning approach. [Internet] [Masters thesis]. Delft University of Technology; 2017. [cited 2020 Aug 07]. Available from: http://resolver.tudelft.nl/uuid:261fb6d5-bcda-471a-907a-e3fdcec20e50.

Council of Science Editors:

Liscio E(. Learning Autonomous Grasping Strategies for a Care Robot: A Machine Learning approach. [Masters Thesis]. Delft University of Technology; 2017. Available from: http://resolver.tudelft.nl/uuid:261fb6d5-bcda-471a-907a-e3fdcec20e50


University of New South Wales

8. Sarker, Chandrama. Mixed Pixel Analysis and Assessment for Flood Mapping.

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

 The most difficult operation in flood inundation mapping using optical flood images is to map the ‘wet’ areas where trees and houses are partly covered… (more)

Subjects/Keywords: Linear Spectral Unmixing; Support Vector Machines; Flood Mapping; Mixed Pixel; Extended Support Vector Machines

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APA (6th Edition):

Sarker, C. (2014). Mixed Pixel Analysis and Assessment for Flood Mapping. (Masters Thesis). University of New South Wales. Retrieved from http://handle.unsw.edu.au/1959.4/53923 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:12633/SOURCE02?view=true

Chicago Manual of Style (16th Edition):

Sarker, Chandrama. “Mixed Pixel Analysis and Assessment for Flood Mapping.” 2014. Masters Thesis, University of New South Wales. Accessed August 07, 2020. http://handle.unsw.edu.au/1959.4/53923 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:12633/SOURCE02?view=true.

MLA Handbook (7th Edition):

Sarker, Chandrama. “Mixed Pixel Analysis and Assessment for Flood Mapping.” 2014. Web. 07 Aug 2020.

Vancouver:

Sarker C. Mixed Pixel Analysis and Assessment for Flood Mapping. [Internet] [Masters thesis]. University of New South Wales; 2014. [cited 2020 Aug 07]. Available from: http://handle.unsw.edu.au/1959.4/53923 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:12633/SOURCE02?view=true.

Council of Science Editors:

Sarker C. Mixed Pixel Analysis and Assessment for Flood Mapping. [Masters Thesis]. University of New South Wales; 2014. Available from: http://handle.unsw.edu.au/1959.4/53923 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:12633/SOURCE02?view=true


Uppsala University

9. Hedlund, Henrik. Predicting Satisfaction in Customer Support Chat : Opinion Mining as a Binary Classification Problem.

Degree: Linguistics and Philology, 2016, Uppsala University

  The study explores binary classification with Support Vector Machines as means to predict a satisfaction score based on customer surveys in the customer supportdomain.… (more)

Subjects/Keywords: Binary Classification; Customer Support; Chat; Opinion Mining; Support Vector Machines

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APA (6th Edition):

Hedlund, H. (2016). Predicting Satisfaction in Customer Support Chat : Opinion Mining as a Binary Classification Problem. (Thesis). Uppsala University. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-300165

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):

Hedlund, Henrik. “Predicting Satisfaction in Customer Support Chat : Opinion Mining as a Binary Classification Problem.” 2016. Thesis, Uppsala University. Accessed August 07, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-300165.

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

MLA Handbook (7th Edition):

Hedlund, Henrik. “Predicting Satisfaction in Customer Support Chat : Opinion Mining as a Binary Classification Problem.” 2016. Web. 07 Aug 2020.

Vancouver:

Hedlund H. Predicting Satisfaction in Customer Support Chat : Opinion Mining as a Binary Classification Problem. [Internet] [Thesis]. Uppsala University; 2016. [cited 2020 Aug 07]. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-300165.

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

Council of Science Editors:

Hedlund H. Predicting Satisfaction in Customer Support Chat : Opinion Mining as a Binary Classification Problem. [Thesis]. Uppsala University; 2016. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-300165

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

10. Taylor, Aimee Elizabeth. Statistical enhancement of support vector machines.

Degree: PhD, Statistics, 2009, Oregon State University

Support Vector Machines (SVM) and Random Forests (RF) have consistently outperformed other machine learning algorithms on a variety of problems. SVM can be used for… (more)

Subjects/Keywords: Support Vector Machines; Support vector machines

…3 2.1 Support Vector Machines ….... 3 2.2 SVM Classification… …Support Vector Machine 1 Introduction Machine learning is concerned with training machines to… …x5B;0, ). 2.1 Support Vector Machines Support Vector Machines are based on statistical… …are the Support Vector Machine algorithm (SVM) (Vapnik, 1998) and Random… …boundary. This simplest type of SVM is called a linear support vector machine (LSVM)… 

Page 1 Page 2 Page 3 Page 4 Page 5 Page 6 Page 7

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APA (6th Edition):

Taylor, A. E. (2009). Statistical enhancement of support vector machines. (Doctoral Dissertation). Oregon State University. Retrieved from http://hdl.handle.net/1957/11188

Chicago Manual of Style (16th Edition):

Taylor, Aimee Elizabeth. “Statistical enhancement of support vector machines.” 2009. Doctoral Dissertation, Oregon State University. Accessed August 07, 2020. http://hdl.handle.net/1957/11188.

MLA Handbook (7th Edition):

Taylor, Aimee Elizabeth. “Statistical enhancement of support vector machines.” 2009. Web. 07 Aug 2020.

Vancouver:

Taylor AE. Statistical enhancement of support vector machines. [Internet] [Doctoral dissertation]. Oregon State University; 2009. [cited 2020 Aug 07]. Available from: http://hdl.handle.net/1957/11188.

Council of Science Editors:

Taylor AE. Statistical enhancement of support vector machines. [Doctoral Dissertation]. Oregon State University; 2009. Available from: http://hdl.handle.net/1957/11188


Brno University of Technology

11. Oravec, Jakub. Klasifikace dokumentů podle tématu: Document Topic Classification.

Degree: 2018, Brno University of Technology

 This bachelor's thesis deals with automatic document topic classification and provides a brief introduction to this area of research. The first part contains summary of… (more)

Subjects/Keywords: klasifikácia dokumentov podľa témy; support vector machines; trénovacia sada; metriky; document topic classification; support vector machines; training set; metrics

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APA (6th Edition):

Oravec, J. (2018). Klasifikace dokumentů podle tématu: Document Topic Classification. (Thesis). Brno University of Technology. Retrieved from http://hdl.handle.net/11012/55401

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):

Oravec, Jakub. “Klasifikace dokumentů podle tématu: Document Topic Classification.” 2018. Thesis, Brno University of Technology. Accessed August 07, 2020. http://hdl.handle.net/11012/55401.

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

MLA Handbook (7th Edition):

Oravec, Jakub. “Klasifikace dokumentů podle tématu: Document Topic Classification.” 2018. Web. 07 Aug 2020.

Vancouver:

Oravec J. Klasifikace dokumentů podle tématu: Document Topic Classification. [Internet] [Thesis]. Brno University of Technology; 2018. [cited 2020 Aug 07]. Available from: http://hdl.handle.net/11012/55401.

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

Council of Science Editors:

Oravec J. Klasifikace dokumentů podle tématu: Document Topic Classification. [Thesis]. Brno University of Technology; 2018. Available from: http://hdl.handle.net/11012/55401

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


University of Alberta

12. Wang, Baoliang. Automatic Animal Species Identification Based on Camera Trapping Data.

Degree: MS, Department of Computing Science, 2014, University of Alberta

 The classification of animal images based on camera trapping data is an important and challenging task in the domains of computer vision, machine learning and… (more)

Subjects/Keywords: Animal Species Identification; Support Vector Machines; Average Pooling; Fisher Vector; Camera Trapping; Gaussian Mixture Model

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

APA (6th Edition):

Wang, B. (2014). Automatic Animal Species Identification Based on Camera Trapping Data. (Masters Thesis). University of Alberta. Retrieved from https://era.library.ualberta.ca/files/ms35t9138

Chicago Manual of Style (16th Edition):

Wang, Baoliang. “Automatic Animal Species Identification Based on Camera Trapping Data.” 2014. Masters Thesis, University of Alberta. Accessed August 07, 2020. https://era.library.ualberta.ca/files/ms35t9138.

MLA Handbook (7th Edition):

Wang, Baoliang. “Automatic Animal Species Identification Based on Camera Trapping Data.” 2014. Web. 07 Aug 2020.

Vancouver:

Wang B. Automatic Animal Species Identification Based on Camera Trapping Data. [Internet] [Masters thesis]. University of Alberta; 2014. [cited 2020 Aug 07]. Available from: https://era.library.ualberta.ca/files/ms35t9138.

Council of Science Editors:

Wang B. Automatic Animal Species Identification Based on Camera Trapping Data. [Masters Thesis]. University of Alberta; 2014. Available from: https://era.library.ualberta.ca/files/ms35t9138


Penn State University

13. Nag, Abhikesh. Combined Generative-Discriminative Learning for Object Recognition using Local Image Descriptors.

Degree: MS, Electrical Engineering, 2008, Penn State University

 We present a system for scale and affine invariant recognition of vehicular objects in video sequences. We use local descriptors (SIFT keypoints) from image frames… (more)

Subjects/Keywords: support vector machines; mixture models; SIFT features; generative-discriminative learning

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APA (6th Edition):

Nag, A. (2008). Combined Generative-Discriminative Learning for Object Recognition using Local Image Descriptors. (Masters Thesis). Penn State University. Retrieved from https://etda.libraries.psu.edu/catalog/8181

Chicago Manual of Style (16th Edition):

Nag, Abhikesh. “Combined Generative-Discriminative Learning for Object Recognition using Local Image Descriptors.” 2008. Masters Thesis, Penn State University. Accessed August 07, 2020. https://etda.libraries.psu.edu/catalog/8181.

MLA Handbook (7th Edition):

Nag, Abhikesh. “Combined Generative-Discriminative Learning for Object Recognition using Local Image Descriptors.” 2008. Web. 07 Aug 2020.

Vancouver:

Nag A. Combined Generative-Discriminative Learning for Object Recognition using Local Image Descriptors. [Internet] [Masters thesis]. Penn State University; 2008. [cited 2020 Aug 07]. Available from: https://etda.libraries.psu.edu/catalog/8181.

Council of Science Editors:

Nag A. Combined Generative-Discriminative Learning for Object Recognition using Local Image Descriptors. [Masters Thesis]. Penn State University; 2008. Available from: https://etda.libraries.psu.edu/catalog/8181

14. Fernandez, Llamosa Michael. Topological Autocorrelations for Prediction of Protein Conformational Stability and Kinase and Protease Inhibitions : トポロジカル自己相関関数を用いた蛋白質の構造安定性とキナーゼ及びプロテアーゼ阻害の予測.

Degree: 博士(情報工学), 2017, Kyushu Institute of Technology / 九州工業大学

The annotation of protein structure and function from sequence and the prediction of compound’s activity from sketch representations are fundamental goals in bio- and chemoinformatics.… (more)

Subjects/Keywords: QSAR; Drug design; Support vector machines; Genetic algorithm; Protein design

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APA (6th Edition):

Fernandez, L. M. (2017). Topological Autocorrelations for Prediction of Protein Conformational Stability and Kinase and Protease Inhibitions : トポロジカル自己相関関数を用いた蛋白質の構造安定性とキナーゼ及びプロテアーゼ阻害の予測. (Thesis). Kyushu Institute of Technology / 九州工業大学. Retrieved from http://hdl.handle.net/10228/4874

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):

Fernandez, Llamosa Michael. “Topological Autocorrelations for Prediction of Protein Conformational Stability and Kinase and Protease Inhibitions : トポロジカル自己相関関数を用いた蛋白質の構造安定性とキナーゼ及びプロテアーゼ阻害の予測.” 2017. Thesis, Kyushu Institute of Technology / 九州工業大学. Accessed August 07, 2020. http://hdl.handle.net/10228/4874.

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

MLA Handbook (7th Edition):

Fernandez, Llamosa Michael. “Topological Autocorrelations for Prediction of Protein Conformational Stability and Kinase and Protease Inhibitions : トポロジカル自己相関関数を用いた蛋白質の構造安定性とキナーゼ及びプロテアーゼ阻害の予測.” 2017. Web. 07 Aug 2020.

Vancouver:

Fernandez LM. Topological Autocorrelations for Prediction of Protein Conformational Stability and Kinase and Protease Inhibitions : トポロジカル自己相関関数を用いた蛋白質の構造安定性とキナーゼ及びプロテアーゼ阻害の予測. [Internet] [Thesis]. Kyushu Institute of Technology / 九州工業大学; 2017. [cited 2020 Aug 07]. Available from: http://hdl.handle.net/10228/4874.

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

Council of Science Editors:

Fernandez LM. Topological Autocorrelations for Prediction of Protein Conformational Stability and Kinase and Protease Inhibitions : トポロジカル自己相関関数を用いた蛋白質の構造安定性とキナーゼ及びプロテアーゼ阻害の予測. [Thesis]. Kyushu Institute of Technology / 九州工業大学; 2017. Available from: http://hdl.handle.net/10228/4874

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


Texas A&M University

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

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 August 07, 2020. 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. 07 Aug 2020.

Vancouver:

Narayana S. Affect Recognition Using Electroencephalography Features. [Internet] [Thesis]. Texas A&M University; 2017. [cited 2020 Aug 07]. 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

16. Sita Mahalakshmi, T. Some studies on recognition of handwritten Telugu characters; -.

Degree: Computer Engineering, 2011, Acharya Nagarjuna University

Computers are all pervasive essentialities in today s world and technologies that improve our interactions with them are growing at a never before rate. One… (more)

Subjects/Keywords: Computer Science; Neural Networks; Support Vector Machines; Kernel Trick

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APA (6th Edition):

Sita Mahalakshmi, T. (2011). Some studies on recognition of handwritten Telugu characters; -. (Thesis). Acharya Nagarjuna University. Retrieved from http://shodhganga.inflibnet.ac.in/handle/10603/10438

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):

Sita Mahalakshmi, T. “Some studies on recognition of handwritten Telugu characters; -.” 2011. Thesis, Acharya Nagarjuna University. Accessed August 07, 2020. http://shodhganga.inflibnet.ac.in/handle/10603/10438.

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

MLA Handbook (7th Edition):

Sita Mahalakshmi, T. “Some studies on recognition of handwritten Telugu characters; -.” 2011. Web. 07 Aug 2020.

Vancouver:

Sita Mahalakshmi T. Some studies on recognition of handwritten Telugu characters; -. [Internet] [Thesis]. Acharya Nagarjuna University; 2011. [cited 2020 Aug 07]. Available from: http://shodhganga.inflibnet.ac.in/handle/10603/10438.

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

Council of Science Editors:

Sita Mahalakshmi T. Some studies on recognition of handwritten Telugu characters; -. [Thesis]. Acharya Nagarjuna University; 2011. Available from: http://shodhganga.inflibnet.ac.in/handle/10603/10438

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


University of Missouri – Columbia

17. Liu, Yang (Engineer). People re-identification over non-overlapping camera views.

Degree: 2013, University of Missouri – Columbia

 [ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] Person re-identification is a computer vision task of recognizing an individual from similar background across… (more)

Subjects/Keywords: Pattern recognition systems.; Computer vision.; Support vector machines.

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APA (6th Edition):

Liu, Y. (. (2013). People re-identification over non-overlapping camera views. (Thesis). University of Missouri – Columbia. Retrieved from http://hdl.handle.net/10355/43678

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):

Liu, Yang (Engineer). “People re-identification over non-overlapping camera views.” 2013. Thesis, University of Missouri – Columbia. Accessed August 07, 2020. http://hdl.handle.net/10355/43678.

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

MLA Handbook (7th Edition):

Liu, Yang (Engineer). “People re-identification over non-overlapping camera views.” 2013. Web. 07 Aug 2020.

Vancouver:

Liu Y(. People re-identification over non-overlapping camera views. [Internet] [Thesis]. University of Missouri – Columbia; 2013. [cited 2020 Aug 07]. Available from: http://hdl.handle.net/10355/43678.

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

Council of Science Editors:

Liu Y(. People re-identification over non-overlapping camera views. [Thesis]. University of Missouri – Columbia; 2013. Available from: http://hdl.handle.net/10355/43678

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

18. LINS, Isis Didier. Support vector machines and particle swarm optimization applied to reliability prediction .

Degree: 2009, Universidade Federal de Pernambuco

 Confiabilidade é uma métrica crítica para as organizações, uma vez que ela influencia diretamente seus desempenhos face à concorrência e é essencial para a manutenção… (more)

Subjects/Keywords: Previsão de Confiabilidade; Otimização via Nuvens de Partículas; Support Vector Machines

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

APA (6th Edition):

LINS, I. D. (2009). Support vector machines and particle swarm optimization applied to reliability prediction . (Thesis). Universidade Federal de Pernambuco. Retrieved from http://repositorio.ufpe.br/handle/123456789/5996

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):

LINS, Isis Didier. “Support vector machines and particle swarm optimization applied to reliability prediction .” 2009. Thesis, Universidade Federal de Pernambuco. Accessed August 07, 2020. http://repositorio.ufpe.br/handle/123456789/5996.

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

MLA Handbook (7th Edition):

LINS, Isis Didier. “Support vector machines and particle swarm optimization applied to reliability prediction .” 2009. Web. 07 Aug 2020.

Vancouver:

LINS ID. Support vector machines and particle swarm optimization applied to reliability prediction . [Internet] [Thesis]. Universidade Federal de Pernambuco; 2009. [cited 2020 Aug 07]. Available from: http://repositorio.ufpe.br/handle/123456789/5996.

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

Council of Science Editors:

LINS ID. Support vector machines and particle swarm optimization applied to reliability prediction . [Thesis]. Universidade Federal de Pernambuco; 2009. Available from: http://repositorio.ufpe.br/handle/123456789/5996

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


Wayne State University

19. Albousefi, Alhadi Ali. Vehicle Lane Departure Prediction Based On Support Vector Machines.

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

  Advanced driver assistance systems, such as unintentional lane departure warning systems, have recently drawn much attention and R & D efforts. Such a system… (more)

Subjects/Keywords: Prediction; Support vector machines; Unintentional lane departure; Electrical and Computer Engineering

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

APA (6th Edition):

Albousefi, A. A. (2014). Vehicle Lane Departure Prediction Based On Support Vector Machines. (Doctoral Dissertation). Wayne State University. Retrieved from https://digitalcommons.wayne.edu/oa_dissertations/957

Chicago Manual of Style (16th Edition):

Albousefi, Alhadi Ali. “Vehicle Lane Departure Prediction Based On Support Vector Machines.” 2014. Doctoral Dissertation, Wayne State University. Accessed August 07, 2020. https://digitalcommons.wayne.edu/oa_dissertations/957.

MLA Handbook (7th Edition):

Albousefi, Alhadi Ali. “Vehicle Lane Departure Prediction Based On Support Vector Machines.” 2014. Web. 07 Aug 2020.

Vancouver:

Albousefi AA. Vehicle Lane Departure Prediction Based On Support Vector Machines. [Internet] [Doctoral dissertation]. Wayne State University; 2014. [cited 2020 Aug 07]. Available from: https://digitalcommons.wayne.edu/oa_dissertations/957.

Council of Science Editors:

Albousefi AA. Vehicle Lane Departure Prediction Based On Support Vector Machines. [Doctoral Dissertation]. Wayne State University; 2014. Available from: https://digitalcommons.wayne.edu/oa_dissertations/957


Virginia Tech

20. Bhaduri, Sreyoshi. Algorithm to enable intelligent rail break detection.

Degree: MS, Mechanical Engineering, 2013, Virginia Tech

 Wavelet intensity based algorithm developed previously at VirginiaTech has been furthered and paired with an SVM based classifier. The wavelet intensity algorithm acts as a… (more)

Subjects/Keywords: Support Vector Machines; crossing and track safety; rail break detection

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APA (6th Edition):

Bhaduri, S. (2013). Algorithm to enable intelligent rail break detection. (Masters Thesis). Virginia Tech. Retrieved from http://hdl.handle.net/10919/78080

Chicago Manual of Style (16th Edition):

Bhaduri, Sreyoshi. “Algorithm to enable intelligent rail break detection.” 2013. Masters Thesis, Virginia Tech. Accessed August 07, 2020. http://hdl.handle.net/10919/78080.

MLA Handbook (7th Edition):

Bhaduri, Sreyoshi. “Algorithm to enable intelligent rail break detection.” 2013. Web. 07 Aug 2020.

Vancouver:

Bhaduri S. Algorithm to enable intelligent rail break detection. [Internet] [Masters thesis]. Virginia Tech; 2013. [cited 2020 Aug 07]. Available from: http://hdl.handle.net/10919/78080.

Council of Science Editors:

Bhaduri S. Algorithm to enable intelligent rail break detection. [Masters Thesis]. Virginia Tech; 2013. Available from: http://hdl.handle.net/10919/78080


University of Adelaide

21. Chew, Hong Gunn. Support vector machines with dual error extensions for target detection and object recognition.

Degree: 2013, University of Adelaide

 The Support Vector Machine (SVM) is a binary classification paradigm based on statistical learning. It is an important tool in object detection and pattern recognition,… (more)

Subjects/Keywords: support vector machines; target detection; dual error extensions; nu-svm; multiclass

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

APA (6th Edition):

Chew, H. G. (2013). Support vector machines with dual error extensions for target detection and object recognition. (Thesis). University of Adelaide. Retrieved from http://hdl.handle.net/2440/96169

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):

Chew, Hong Gunn. “Support vector machines with dual error extensions for target detection and object recognition.” 2013. Thesis, University of Adelaide. Accessed August 07, 2020. http://hdl.handle.net/2440/96169.

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

MLA Handbook (7th Edition):

Chew, Hong Gunn. “Support vector machines with dual error extensions for target detection and object recognition.” 2013. Web. 07 Aug 2020.

Vancouver:

Chew HG. Support vector machines with dual error extensions for target detection and object recognition. [Internet] [Thesis]. University of Adelaide; 2013. [cited 2020 Aug 07]. Available from: http://hdl.handle.net/2440/96169.

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

Council of Science Editors:

Chew HG. Support vector machines with dual error extensions for target detection and object recognition. [Thesis]. University of Adelaide; 2013. Available from: http://hdl.handle.net/2440/96169

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


Rhodes University

22. Tsilo, Lipontseng Cecilia. Protein secondary structure prediction using neural networks and support vector machines.

Degree: Faculty of Science, Statistics, 2009, Rhodes University

 Predicting the secondary structure of proteins is important in biochemistry because the 3D structure can be determined from the local folds that are found in… (more)

Subjects/Keywords: Neural networks (Computer science); Support vector machines; Proteins  – Structure  – Mathematical models

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

APA (6th Edition):

Tsilo, L. C. (2009). Protein secondary structure prediction using neural networks and support vector machines. (Thesis). Rhodes University. Retrieved from http://hdl.handle.net/10962/d1002809

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):

Tsilo, Lipontseng Cecilia. “Protein secondary structure prediction using neural networks and support vector machines.” 2009. Thesis, Rhodes University. Accessed August 07, 2020. http://hdl.handle.net/10962/d1002809.

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

MLA Handbook (7th Edition):

Tsilo, Lipontseng Cecilia. “Protein secondary structure prediction using neural networks and support vector machines.” 2009. Web. 07 Aug 2020.

Vancouver:

Tsilo LC. Protein secondary structure prediction using neural networks and support vector machines. [Internet] [Thesis]. Rhodes University; 2009. [cited 2020 Aug 07]. Available from: http://hdl.handle.net/10962/d1002809.

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

Council of Science Editors:

Tsilo LC. Protein secondary structure prediction using neural networks and support vector machines. [Thesis]. Rhodes University; 2009. Available from: http://hdl.handle.net/10962/d1002809

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


University of Waterloo

23. Rashwan, Abdullah. Automatic Driver Fatigue Monitoring Using Hidden Markov Models and Bayesian Networks.

Degree: 2013, University of Waterloo

 The automotive industry is growing bigger each year. The central concern for any automotive company is driver and passenger safety. Many automotive companies have developed… (more)

Subjects/Keywords: Driver fatigue detection; Hidden Markov Models; Support Vector Machines; Bayesian networks

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APA (6th Edition):

Rashwan, A. (2013). Automatic Driver Fatigue Monitoring Using Hidden Markov Models and Bayesian Networks. (Thesis). University of Waterloo. Retrieved from http://hdl.handle.net/10012/8082

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):

Rashwan, Abdullah. “Automatic Driver Fatigue Monitoring Using Hidden Markov Models and Bayesian Networks.” 2013. Thesis, University of Waterloo. Accessed August 07, 2020. http://hdl.handle.net/10012/8082.

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

MLA Handbook (7th Edition):

Rashwan, Abdullah. “Automatic Driver Fatigue Monitoring Using Hidden Markov Models and Bayesian Networks.” 2013. Web. 07 Aug 2020.

Vancouver:

Rashwan A. Automatic Driver Fatigue Monitoring Using Hidden Markov Models and Bayesian Networks. [Internet] [Thesis]. University of Waterloo; 2013. [cited 2020 Aug 07]. Available from: http://hdl.handle.net/10012/8082.

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

Council of Science Editors:

Rashwan A. Automatic Driver Fatigue Monitoring Using Hidden Markov Models and Bayesian Networks. [Thesis]. University of Waterloo; 2013. Available from: http://hdl.handle.net/10012/8082

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


University of Oklahoma

24. Gilbert, Robin Charles. Unconstrained Learning Machines.

Degree: PhD, 2010, University of Oklahoma

 ULMs are applied to a variety of problems in manufacturing engineering and in meteorology. The robust nonlinear nonparametric interpolation abilities of ULMs allow for the… (more)

Subjects/Keywords: Machine learning; Neural networks (Computer science); Support vector machines

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APA (6th Edition):

Gilbert, R. C. (2010). Unconstrained Learning Machines. (Doctoral Dissertation). University of Oklahoma. Retrieved from http://hdl.handle.net/11244/319132

Chicago Manual of Style (16th Edition):

Gilbert, Robin Charles. “Unconstrained Learning Machines.” 2010. Doctoral Dissertation, University of Oklahoma. Accessed August 07, 2020. http://hdl.handle.net/11244/319132.

MLA Handbook (7th Edition):

Gilbert, Robin Charles. “Unconstrained Learning Machines.” 2010. Web. 07 Aug 2020.

Vancouver:

Gilbert RC. Unconstrained Learning Machines. [Internet] [Doctoral dissertation]. University of Oklahoma; 2010. [cited 2020 Aug 07]. Available from: http://hdl.handle.net/11244/319132.

Council of Science Editors:

Gilbert RC. Unconstrained Learning Machines. [Doctoral Dissertation]. University of Oklahoma; 2010. Available from: http://hdl.handle.net/11244/319132


California State University – Channel Islands

25. Mayorga, David M. A Support Vector Machine Approach to Analyzing Aftermarket Ticket Sales .

Degree: 2011, California State University – Channel Islands

Support Vector Machines (SVMs) are used to linearly separate & classify data. If our data is not linearly separable, we use a kernel function to… (more)

Subjects/Keywords: Support vector machines; SVM; SVMs; Ticket; Resale; Analyst; Mathematics thesis

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APA (6th Edition):

Mayorga, D. M. (2011). A Support Vector Machine Approach to Analyzing Aftermarket Ticket Sales . (Thesis). California State University – Channel Islands. Retrieved from http://hdl.handle.net/10139/4993

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):

Mayorga, David M. “A Support Vector Machine Approach to Analyzing Aftermarket Ticket Sales .” 2011. Thesis, California State University – Channel Islands. Accessed August 07, 2020. http://hdl.handle.net/10139/4993.

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

MLA Handbook (7th Edition):

Mayorga, David M. “A Support Vector Machine Approach to Analyzing Aftermarket Ticket Sales .” 2011. Web. 07 Aug 2020.

Vancouver:

Mayorga DM. A Support Vector Machine Approach to Analyzing Aftermarket Ticket Sales . [Internet] [Thesis]. California State University – Channel Islands; 2011. [cited 2020 Aug 07]. Available from: http://hdl.handle.net/10139/4993.

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

Council of Science Editors:

Mayorga DM. A Support Vector Machine Approach to Analyzing Aftermarket Ticket Sales . [Thesis]. California State University – Channel Islands; 2011. Available from: http://hdl.handle.net/10139/4993

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


Brunel University

26. Haddi, Emma. Sentiment analysis : text, pre-processing, reader views and cross domains.

Degree: PhD, 2015, Brunel University

 Sentiment analysis has emerged as a field that has attracted a significant amount of attention since it has a wide variety of applications that could… (more)

Subjects/Keywords: 006.3; Movie reviews; Financial news; Support vector machines (SVM)

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APA (6th Edition):

Haddi, E. (2015). Sentiment analysis : text, pre-processing, reader views and cross domains. (Doctoral Dissertation). Brunel University. Retrieved from http://bura.brunel.ac.uk/handle/2438/11196 ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.659238

Chicago Manual of Style (16th Edition):

Haddi, Emma. “Sentiment analysis : text, pre-processing, reader views and cross domains.” 2015. Doctoral Dissertation, Brunel University. Accessed August 07, 2020. http://bura.brunel.ac.uk/handle/2438/11196 ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.659238.

MLA Handbook (7th Edition):

Haddi, Emma. “Sentiment analysis : text, pre-processing, reader views and cross domains.” 2015. Web. 07 Aug 2020.

Vancouver:

Haddi E. Sentiment analysis : text, pre-processing, reader views and cross domains. [Internet] [Doctoral dissertation]. Brunel University; 2015. [cited 2020 Aug 07]. Available from: http://bura.brunel.ac.uk/handle/2438/11196 ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.659238.

Council of Science Editors:

Haddi E. Sentiment analysis : text, pre-processing, reader views and cross domains. [Doctoral Dissertation]. Brunel University; 2015. Available from: http://bura.brunel.ac.uk/handle/2438/11196 ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.659238


Tampere University

27. Ke, Da. Conceptual design on computer sentencing simulation based on SVM .

Degree: 2018, Tampere University

 The criminal law in China is a relatively uncertain statutory punishment law, and the judge exercise the equitable discretion within the extent for discretionary action… (more)

Subjects/Keywords: sentencing; sentencing circumstances sentencing method; machine learning; support vector machines.

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APA (6th Edition):

Ke, D. (2018). Conceptual design on computer sentencing simulation based on SVM . (Masters Thesis). Tampere University. Retrieved from https://trepo.tuni.fi/handle/10024/103868

Chicago Manual of Style (16th Edition):

Ke, Da. “Conceptual design on computer sentencing simulation based on SVM .” 2018. Masters Thesis, Tampere University. Accessed August 07, 2020. https://trepo.tuni.fi/handle/10024/103868.

MLA Handbook (7th Edition):

Ke, Da. “Conceptual design on computer sentencing simulation based on SVM .” 2018. Web. 07 Aug 2020.

Vancouver:

Ke D. Conceptual design on computer sentencing simulation based on SVM . [Internet] [Masters thesis]. Tampere University; 2018. [cited 2020 Aug 07]. Available from: https://trepo.tuni.fi/handle/10024/103868.

Council of Science Editors:

Ke D. Conceptual design on computer sentencing simulation based on SVM . [Masters Thesis]. Tampere University; 2018. Available from: https://trepo.tuni.fi/handle/10024/103868


New Jersey Institute of Technology

28. Shahidain, Seif. Ranking single nucleotide polymorphisms with support vector regression in continuous phenotypes.

Degree: MSin Computational Biology - (M.S.), Mathematical Sciences, 2011, New Jersey Institute of Technology

Support vector machines (SVM) have been used to improve the ranking of single nucleotide polymorphisms (SNPs) over traditional chi-square tests in disease case studies… (more)

Subjects/Keywords: Support vector machines; Single nucleotide polymorphisms; Continuous phenotypes; Biostatistics; Computer Sciences

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APA (6th Edition):

Shahidain, S. (2011). Ranking single nucleotide polymorphisms with support vector regression in continuous phenotypes. (Thesis). New Jersey Institute of Technology. Retrieved from https://digitalcommons.njit.edu/theses/95

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):

Shahidain, Seif. “Ranking single nucleotide polymorphisms with support vector regression in continuous phenotypes.” 2011. Thesis, New Jersey Institute of Technology. Accessed August 07, 2020. https://digitalcommons.njit.edu/theses/95.

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

MLA Handbook (7th Edition):

Shahidain, Seif. “Ranking single nucleotide polymorphisms with support vector regression in continuous phenotypes.” 2011. Web. 07 Aug 2020.

Vancouver:

Shahidain S. Ranking single nucleotide polymorphisms with support vector regression in continuous phenotypes. [Internet] [Thesis]. New Jersey Institute of Technology; 2011. [cited 2020 Aug 07]. Available from: https://digitalcommons.njit.edu/theses/95.

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

Council of Science Editors:

Shahidain S. Ranking single nucleotide polymorphisms with support vector regression in continuous phenotypes. [Thesis]. New Jersey Institute of Technology; 2011. Available from: https://digitalcommons.njit.edu/theses/95

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


Rutgers University

29. Varkey, John Paul, 1984-. Human motion recognition using a wireless wearable system.

Degree: MS, Electrical and Computer Engineering, 2010, Rutgers University

 The future of human computer interaction systems lies in how intelligently these systems can take into account the user's context, that is, how well the… (more)

Subjects/Keywords: Human activity recognition; Wearable computers; Gyroscopes; Support vector machines; Accelerometers

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

APA (6th Edition):

Varkey, John Paul, 1. (2010). Human motion recognition using a wireless wearable system. (Masters Thesis). Rutgers University. Retrieved from http://hdl.rutgers.edu/1782.1/rucore10001600001.ETD.000056812

Chicago Manual of Style (16th Edition):

Varkey, John Paul, 1984-. “Human motion recognition using a wireless wearable system.” 2010. Masters Thesis, Rutgers University. Accessed August 07, 2020. http://hdl.rutgers.edu/1782.1/rucore10001600001.ETD.000056812.

MLA Handbook (7th Edition):

Varkey, John Paul, 1984-. “Human motion recognition using a wireless wearable system.” 2010. Web. 07 Aug 2020.

Vancouver:

Varkey, John Paul 1. Human motion recognition using a wireless wearable system. [Internet] [Masters thesis]. Rutgers University; 2010. [cited 2020 Aug 07]. Available from: http://hdl.rutgers.edu/1782.1/rucore10001600001.ETD.000056812.

Council of Science Editors:

Varkey, John Paul 1. Human motion recognition using a wireless wearable system. [Masters Thesis]. Rutgers University; 2010. Available from: http://hdl.rutgers.edu/1782.1/rucore10001600001.ETD.000056812


KTH

30. Norgren, Nils Dahlbom. Relation Classification Between the Extracted Entities of Swedish Verdicts.

Degree: Computer Science and Communication (CSC), 2017, KTH

This master thesis investigated how well a multiclass support vector machine approach is at classifying a fixed number of interpersonal relations between extracted entities… (more)

Subjects/Keywords: Support Vector Machines; Machine Learning; Relation Classification; Computer Sciences; Datavetenskap (datalogi)

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

APA (6th Edition):

Norgren, N. D. (2017). Relation Classification Between the Extracted Entities of Swedish Verdicts. (Thesis). KTH. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-206829

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):

Norgren, Nils Dahlbom. “Relation Classification Between the Extracted Entities of Swedish Verdicts.” 2017. Thesis, KTH. Accessed August 07, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-206829.

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

MLA Handbook (7th Edition):

Norgren, Nils Dahlbom. “Relation Classification Between the Extracted Entities of Swedish Verdicts.” 2017. Web. 07 Aug 2020.

Vancouver:

Norgren ND. Relation Classification Between the Extracted Entities of Swedish Verdicts. [Internet] [Thesis]. KTH; 2017. [cited 2020 Aug 07]. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-206829.

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

Council of Science Editors:

Norgren ND. Relation Classification Between the Extracted Entities of Swedish Verdicts. [Thesis]. KTH; 2017. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-206829

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

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