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You searched for subject:(machine learning models). Showing records 121 – 150 of 394 total matches.

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University of Minnesota

121. Zhang, Wei. Computational Analysis of Transcript Interactions and Variants in Cancer.

Degree: PhD, Computer Science, 2015, University of Minnesota

 New sequencing and array technologies for transcriptome-wide profiling of RNAs have greatly promoted the interest in gene and isoform-based functional characterizations of a cellular system.… (more)

Subjects/Keywords: Alternative Polyadenylation; Alternative Splicing; Cancer Transcriptome; Machine Learning; Network-based models; RNA-Seq

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APA (6th Edition):

Zhang, W. (2015). Computational Analysis of Transcript Interactions and Variants in Cancer. (Doctoral Dissertation). University of Minnesota. Retrieved from http://hdl.handle.net/11299/177090

Chicago Manual of Style (16th Edition):

Zhang, Wei. “Computational Analysis of Transcript Interactions and Variants in Cancer.” 2015. Doctoral Dissertation, University of Minnesota. Accessed August 20, 2019. http://hdl.handle.net/11299/177090.

MLA Handbook (7th Edition):

Zhang, Wei. “Computational Analysis of Transcript Interactions and Variants in Cancer.” 2015. Web. 20 Aug 2019.

Vancouver:

Zhang W. Computational Analysis of Transcript Interactions and Variants in Cancer. [Internet] [Doctoral dissertation]. University of Minnesota; 2015. [cited 2019 Aug 20]. Available from: http://hdl.handle.net/11299/177090.

Council of Science Editors:

Zhang W. Computational Analysis of Transcript Interactions and Variants in Cancer. [Doctoral Dissertation]. University of Minnesota; 2015. Available from: http://hdl.handle.net/11299/177090


University of New Mexico

122. Roy, Sushmita. Learning condition-specific networks.

Degree: Department of Computer Science, 2009, University of New Mexico

 Condition-specific cellular networks are networks of genes and proteins that describe functional interactions among genes occurring under different environmental conditions. These networks provide a systems-level… (more)

Subjects/Keywords: Machine learning; Computational Biology; Probabilistic graphical models; Gene expression; Condition-specific response

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APA (6th Edition):

Roy, S. (2009). Learning condition-specific networks. (Doctoral Dissertation). University of New Mexico. Retrieved from http://hdl.handle.net/1928/10331

Chicago Manual of Style (16th Edition):

Roy, Sushmita. “Learning condition-specific networks.” 2009. Doctoral Dissertation, University of New Mexico. Accessed August 20, 2019. http://hdl.handle.net/1928/10331.

MLA Handbook (7th Edition):

Roy, Sushmita. “Learning condition-specific networks.” 2009. Web. 20 Aug 2019.

Vancouver:

Roy S. Learning condition-specific networks. [Internet] [Doctoral dissertation]. University of New Mexico; 2009. [cited 2019 Aug 20]. Available from: http://hdl.handle.net/1928/10331.

Council of Science Editors:

Roy S. Learning condition-specific networks. [Doctoral Dissertation]. University of New Mexico; 2009. Available from: http://hdl.handle.net/1928/10331


University of Illinois – Urbana-Champaign

123. Kulkarni, Chinmay Sanjay. Sharing economy-based on-demand peer-to-peer tutoring and resource sharing.

Degree: MS, Computer Science, 2016, University of Illinois – Urbana-Champaign

 The sharing economy is a socio-economic ecosystem built around the sharing of human and physical resources. This is considered to be a new and alternate… (more)

Subjects/Keywords: Sharing Economy; Incentive Models; Game Theory; Stable Matching; Machine Learning; Data Mining; Social Network Analysis

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APA (6th Edition):

Kulkarni, C. S. (2016). Sharing economy-based on-demand peer-to-peer tutoring and resource sharing. (Thesis). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/92958

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):

Kulkarni, Chinmay Sanjay. “Sharing economy-based on-demand peer-to-peer tutoring and resource sharing.” 2016. Thesis, University of Illinois – Urbana-Champaign. Accessed August 20, 2019. http://hdl.handle.net/2142/92958.

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

MLA Handbook (7th Edition):

Kulkarni, Chinmay Sanjay. “Sharing economy-based on-demand peer-to-peer tutoring and resource sharing.” 2016. Web. 20 Aug 2019.

Vancouver:

Kulkarni CS. Sharing economy-based on-demand peer-to-peer tutoring and resource sharing. [Internet] [Thesis]. University of Illinois – Urbana-Champaign; 2016. [cited 2019 Aug 20]. Available from: http://hdl.handle.net/2142/92958.

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

Council of Science Editors:

Kulkarni CS. Sharing economy-based on-demand peer-to-peer tutoring and resource sharing. [Thesis]. University of Illinois – Urbana-Champaign; 2016. Available from: http://hdl.handle.net/2142/92958

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


Australian National University

124. Kamenetsky, Dmitry. Ising Graphical Model .

Degree: 2010, Australian National University

 The Ising model is an important model in statistical physics, with over 10,000 papers published on the topic. This model assumes binary variables and only… (more)

Subjects/Keywords: Ising model; graphical models; computer vision; machine learning; image segmentation; computer go

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APA (6th Edition):

Kamenetsky, D. (2010). Ising Graphical Model . (Thesis). Australian National University. Retrieved from http://hdl.handle.net/1885/49338

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):

Kamenetsky, Dmitry. “Ising Graphical Model .” 2010. Thesis, Australian National University. Accessed August 20, 2019. http://hdl.handle.net/1885/49338.

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

MLA Handbook (7th Edition):

Kamenetsky, Dmitry. “Ising Graphical Model .” 2010. Web. 20 Aug 2019.

Vancouver:

Kamenetsky D. Ising Graphical Model . [Internet] [Thesis]. Australian National University; 2010. [cited 2019 Aug 20]. Available from: http://hdl.handle.net/1885/49338.

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

Council of Science Editors:

Kamenetsky D. Ising Graphical Model . [Thesis]. Australian National University; 2010. Available from: http://hdl.handle.net/1885/49338

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


University of Johannesburg

125. Corregedor, Antonio Rodrigues. Laser based mapping of an unknown environment.

Degree: 2014, University of Johannesburg

M.Ing. (Electrical and Electronic Engineering)

This dissertation deals with the mapping of an unknown environment. Mapping of an environment can be accomplished by asking the… (more)

Subjects/Keywords: Laser based mapping; Algorithms; Simulation methods & models; Mappings (Mathematics); Environmental mapping; Machine learning

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APA (6th Edition):

Corregedor, A. R. (2014). Laser based mapping of an unknown environment. (Thesis). University of Johannesburg. Retrieved from http://hdl.handle.net/10210/9690

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):

Corregedor, Antonio Rodrigues. “Laser based mapping of an unknown environment.” 2014. Thesis, University of Johannesburg. Accessed August 20, 2019. http://hdl.handle.net/10210/9690.

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

MLA Handbook (7th Edition):

Corregedor, Antonio Rodrigues. “Laser based mapping of an unknown environment.” 2014. Web. 20 Aug 2019.

Vancouver:

Corregedor AR. Laser based mapping of an unknown environment. [Internet] [Thesis]. University of Johannesburg; 2014. [cited 2019 Aug 20]. Available from: http://hdl.handle.net/10210/9690.

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

Council of Science Editors:

Corregedor AR. Laser based mapping of an unknown environment. [Thesis]. University of Johannesburg; 2014. Available from: http://hdl.handle.net/10210/9690

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


University of Iowa

126. Zeng, Yaohui. Scalable sparse machine learning methods for big data.

Degree: PhD, Biostatistics, 2017, University of Iowa

  Sparse machine learning models have become increasingly popular in analyzing high-dimensional data. With the evolving era of Big Data, ultrahigh-dimensional, large-scale data sets are… (more)

Subjects/Keywords: Big data; Feature screening; High-dimensional statistics; Lasso-type models; Sparse machine learning; Biostatistics

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APA (6th Edition):

Zeng, Y. (2017). Scalable sparse machine learning methods for big data. (Doctoral Dissertation). University of Iowa. Retrieved from https://ir.uiowa.edu/etd/6021

Chicago Manual of Style (16th Edition):

Zeng, Yaohui. “Scalable sparse machine learning methods for big data.” 2017. Doctoral Dissertation, University of Iowa. Accessed August 20, 2019. https://ir.uiowa.edu/etd/6021.

MLA Handbook (7th Edition):

Zeng, Yaohui. “Scalable sparse machine learning methods for big data.” 2017. Web. 20 Aug 2019.

Vancouver:

Zeng Y. Scalable sparse machine learning methods for big data. [Internet] [Doctoral dissertation]. University of Iowa; 2017. [cited 2019 Aug 20]. Available from: https://ir.uiowa.edu/etd/6021.

Council of Science Editors:

Zeng Y. Scalable sparse machine learning methods for big data. [Doctoral Dissertation]. University of Iowa; 2017. Available from: https://ir.uiowa.edu/etd/6021

127. Alt, Samantha. Learning Approaches to Analog and Mixed Signal Verification and Analysis.

Degree: 2015, University of California – eScholarship, University of California

 The increased integration and interaction of analog and digital components within a system has amplified the need for a fast, automated, combined analog, and digital… (more)

Subjects/Keywords: Computer engineering; Analog Circuit; Automation; Behavior Models; Circuit Partitioning; Machine Learning; Verification

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APA (6th Edition):

Alt, S. (2015). Learning Approaches to Analog and Mixed Signal Verification and Analysis. (Thesis). University of California – eScholarship, University of California. Retrieved from http://www.escholarship.org/uc/item/1rj898j8

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):

Alt, Samantha. “Learning Approaches to Analog and Mixed Signal Verification and Analysis.” 2015. Thesis, University of California – eScholarship, University of California. Accessed August 20, 2019. http://www.escholarship.org/uc/item/1rj898j8.

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

MLA Handbook (7th Edition):

Alt, Samantha. “Learning Approaches to Analog and Mixed Signal Verification and Analysis.” 2015. Web. 20 Aug 2019.

Vancouver:

Alt S. Learning Approaches to Analog and Mixed Signal Verification and Analysis. [Internet] [Thesis]. University of California – eScholarship, University of California; 2015. [cited 2019 Aug 20]. Available from: http://www.escholarship.org/uc/item/1rj898j8.

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

Council of Science Editors:

Alt S. Learning Approaches to Analog and Mixed Signal Verification and Analysis. [Thesis]. University of California – eScholarship, University of California; 2015. Available from: http://www.escholarship.org/uc/item/1rj898j8

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


California State University – Sacramento

128. Jain, Anshul. Comparative analysis of indoor localization using machine learning models.

Degree: MS, Computer Science, 2019, California State University – Sacramento

 Indoor localization has become one of the most talked about services in today???s technology. We have observed that there have been huge demand of Indoor… (more)

Subjects/Keywords: Received signal strength; Test error of machine learning models on CPU; WiFi fingerprinting method

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APA (6th Edition):

Jain, A. (2019). Comparative analysis of indoor localization using machine learning models. (Masters Thesis). California State University – Sacramento. Retrieved from http://hdl.handle.net/10211.3/210277

Chicago Manual of Style (16th Edition):

Jain, Anshul. “Comparative analysis of indoor localization using machine learning models.” 2019. Masters Thesis, California State University – Sacramento. Accessed August 20, 2019. http://hdl.handle.net/10211.3/210277.

MLA Handbook (7th Edition):

Jain, Anshul. “Comparative analysis of indoor localization using machine learning models.” 2019. Web. 20 Aug 2019.

Vancouver:

Jain A. Comparative analysis of indoor localization using machine learning models. [Internet] [Masters thesis]. California State University – Sacramento; 2019. [cited 2019 Aug 20]. Available from: http://hdl.handle.net/10211.3/210277.

Council of Science Editors:

Jain A. Comparative analysis of indoor localization using machine learning models. [Masters Thesis]. California State University – Sacramento; 2019. Available from: http://hdl.handle.net/10211.3/210277

129. Cameron, Christopher J.F. Tissue-to-plasma Partition Coefficient Prediction by a Multi-channel Restricted Boltzmann Machine .

Degree: 2014, University of Guelph

 The use of a modified restricted Boltzmann machine (RBM) on the multifactorial pharmacokinetic problem posed by the prediction of tissue-to-plasma partition coefficients (Kp) within a… (more)

Subjects/Keywords: Tissue-to-plasma coefficients; Machine learning; Pharmacokinetics; Restricted Boltzmann Machines; Logistic Regression Models

Page 1

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APA (6th Edition):

Cameron, C. J. F. (2014). Tissue-to-plasma Partition Coefficient Prediction by a Multi-channel Restricted Boltzmann Machine . (Thesis). University of Guelph. Retrieved from https://atrium.lib.uoguelph.ca/xmlui/handle/10214/7854

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):

Cameron, Christopher J F. “Tissue-to-plasma Partition Coefficient Prediction by a Multi-channel Restricted Boltzmann Machine .” 2014. Thesis, University of Guelph. Accessed August 20, 2019. https://atrium.lib.uoguelph.ca/xmlui/handle/10214/7854.

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

MLA Handbook (7th Edition):

Cameron, Christopher J F. “Tissue-to-plasma Partition Coefficient Prediction by a Multi-channel Restricted Boltzmann Machine .” 2014. Web. 20 Aug 2019.

Vancouver:

Cameron CJF. Tissue-to-plasma Partition Coefficient Prediction by a Multi-channel Restricted Boltzmann Machine . [Internet] [Thesis]. University of Guelph; 2014. [cited 2019 Aug 20]. Available from: https://atrium.lib.uoguelph.ca/xmlui/handle/10214/7854.

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

Council of Science Editors:

Cameron CJF. Tissue-to-plasma Partition Coefficient Prediction by a Multi-channel Restricted Boltzmann Machine . [Thesis]. University of Guelph; 2014. Available from: https://atrium.lib.uoguelph.ca/xmlui/handle/10214/7854

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


Florida Atlantic University

130. Duhaney, Janell A. Mining and fusing data for ocean turbine condition monitoring.

Degree: PhD, 2012, Florida Atlantic University

Summary: An ocean turbine extarcts the kinetic energy from ocean currents to generate electricity. Machine Condition Monitoring (MCM) / Prognostic Health Monitoring (PHM) systems allow… (more)

Subjects/Keywords: Marine turbines – Mathematical models; Fluid dynamics; Data mining; Machine learning; Multisensor data fusion

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APA (6th Edition):

Duhaney, J. A. (2012). Mining and fusing data for ocean turbine condition monitoring. (Doctoral Dissertation). Florida Atlantic University. Retrieved from http://purl.flvc.org/FAU/3358556

Chicago Manual of Style (16th Edition):

Duhaney, Janell A. “Mining and fusing data for ocean turbine condition monitoring.” 2012. Doctoral Dissertation, Florida Atlantic University. Accessed August 20, 2019. http://purl.flvc.org/FAU/3358556.

MLA Handbook (7th Edition):

Duhaney, Janell A. “Mining and fusing data for ocean turbine condition monitoring.” 2012. Web. 20 Aug 2019.

Vancouver:

Duhaney JA. Mining and fusing data for ocean turbine condition monitoring. [Internet] [Doctoral dissertation]. Florida Atlantic University; 2012. [cited 2019 Aug 20]. Available from: http://purl.flvc.org/FAU/3358556.

Council of Science Editors:

Duhaney JA. Mining and fusing data for ocean turbine condition monitoring. [Doctoral Dissertation]. Florida Atlantic University; 2012. Available from: http://purl.flvc.org/FAU/3358556


Arizona State University

131. Yang, Tao. Structured Sparse Methods for Imaging Genetics.

Degree: Computer Science, 2017, Arizona State University

 Imaging genetics is an emerging and promising technique that investigates how genetic variations affect brain development, structure, and function. By exploiting disorder-related neuroimaging phenotypes, this… (more)

Subjects/Keywords: Computer science; Imaging Genetics; Machine Learning; Optimization; Sparse Models; Structured Sparse Methods

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APA (6th Edition):

Yang, T. (2017). Structured Sparse Methods for Imaging Genetics. (Doctoral Dissertation). Arizona State University. Retrieved from http://repository.asu.edu/items/42041

Chicago Manual of Style (16th Edition):

Yang, Tao. “Structured Sparse Methods for Imaging Genetics.” 2017. Doctoral Dissertation, Arizona State University. Accessed August 20, 2019. http://repository.asu.edu/items/42041.

MLA Handbook (7th Edition):

Yang, Tao. “Structured Sparse Methods for Imaging Genetics.” 2017. Web. 20 Aug 2019.

Vancouver:

Yang T. Structured Sparse Methods for Imaging Genetics. [Internet] [Doctoral dissertation]. Arizona State University; 2017. [cited 2019 Aug 20]. Available from: http://repository.asu.edu/items/42041.

Council of Science Editors:

Yang T. Structured Sparse Methods for Imaging Genetics. [Doctoral Dissertation]. Arizona State University; 2017. Available from: http://repository.asu.edu/items/42041


University of Michigan

132. Kaja, Nevrus. Artificial Intelligence and Cybersecurity: Building an Automotive Cybersecurity Framework Using Machine Learning Algorithms.

Degree: PhD, College of Engineering & Computer Science, 2019, University of Michigan

 Automotive technology has continued to advance in many aspects. As an outcome of such advancements, autonomous vehicles are closer to commercialization and have brought to… (more)

Subjects/Keywords: Artificial intelligence; Cybersecurity; Machine learning; Fuzzy logic; Threat models; Automotive; Electrical and Computer Engineering

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APA (6th Edition):

Kaja, N. (2019). Artificial Intelligence and Cybersecurity: Building an Automotive Cybersecurity Framework Using Machine Learning Algorithms. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/149467

Chicago Manual of Style (16th Edition):

Kaja, Nevrus. “Artificial Intelligence and Cybersecurity: Building an Automotive Cybersecurity Framework Using Machine Learning Algorithms.” 2019. Doctoral Dissertation, University of Michigan. Accessed August 20, 2019. http://hdl.handle.net/2027.42/149467.

MLA Handbook (7th Edition):

Kaja, Nevrus. “Artificial Intelligence and Cybersecurity: Building an Automotive Cybersecurity Framework Using Machine Learning Algorithms.” 2019. Web. 20 Aug 2019.

Vancouver:

Kaja N. Artificial Intelligence and Cybersecurity: Building an Automotive Cybersecurity Framework Using Machine Learning Algorithms. [Internet] [Doctoral dissertation]. University of Michigan; 2019. [cited 2019 Aug 20]. Available from: http://hdl.handle.net/2027.42/149467.

Council of Science Editors:

Kaja N. Artificial Intelligence and Cybersecurity: Building an Automotive Cybersecurity Framework Using Machine Learning Algorithms. [Doctoral Dissertation]. University of Michigan; 2019. Available from: http://hdl.handle.net/2027.42/149467


University of Edinburgh

133. Kapourani, Chantriolnt Andreas. Spatial statistical modelling of epigenomic variability.

Degree: PhD, 2019, University of Edinburgh

 Each cell in our body carries the same genetic information encoded in the DNA, yet the human organism contains hundreds of cell types which differ… (more)

Subjects/Keywords: DNA methylation; machine learning; Bayesian modelling; epigenetics; generative probabilistic models; gene expression; computational biology

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APA (6th Edition):

Kapourani, C. A. (2019). Spatial statistical modelling of epigenomic variability. (Doctoral Dissertation). University of Edinburgh. Retrieved from http://hdl.handle.net/1842/35647

Chicago Manual of Style (16th Edition):

Kapourani, Chantriolnt Andreas. “Spatial statistical modelling of epigenomic variability.” 2019. Doctoral Dissertation, University of Edinburgh. Accessed August 20, 2019. http://hdl.handle.net/1842/35647.

MLA Handbook (7th Edition):

Kapourani, Chantriolnt Andreas. “Spatial statistical modelling of epigenomic variability.” 2019. Web. 20 Aug 2019.

Vancouver:

Kapourani CA. Spatial statistical modelling of epigenomic variability. [Internet] [Doctoral dissertation]. University of Edinburgh; 2019. [cited 2019 Aug 20]. Available from: http://hdl.handle.net/1842/35647.

Council of Science Editors:

Kapourani CA. Spatial statistical modelling of epigenomic variability. [Doctoral Dissertation]. University of Edinburgh; 2019. Available from: http://hdl.handle.net/1842/35647


San Jose State University

134. Koppaka, Ravali. MACHINE LEARNING IN CROP CLASSIFICATION OF TEMPORAL MULTISPECTRAL SATELLITE IMAGE.

Degree: MS, Computer Science, 2019, San Jose State University

  Recently, there has been a remarkable growth in Artificial Intelligence (AI) with the development of efficient AI models and high-power computational resources for processing… (more)

Subjects/Keywords: crop classification; india; machine learning models; satellite images; Artificial Intelligence and Robotics; Other Computer Sciences

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APA (6th Edition):

Koppaka, R. (2019). MACHINE LEARNING IN CROP CLASSIFICATION OF TEMPORAL MULTISPECTRAL SATELLITE IMAGE. (Masters Thesis). San Jose State University. Retrieved from https://doi.org/10.31979/etd.z5tm-9zfg ; https://scholarworks.sjsu.edu/etd_projects/734

Chicago Manual of Style (16th Edition):

Koppaka, Ravali. “MACHINE LEARNING IN CROP CLASSIFICATION OF TEMPORAL MULTISPECTRAL SATELLITE IMAGE.” 2019. Masters Thesis, San Jose State University. Accessed August 20, 2019. https://doi.org/10.31979/etd.z5tm-9zfg ; https://scholarworks.sjsu.edu/etd_projects/734.

MLA Handbook (7th Edition):

Koppaka, Ravali. “MACHINE LEARNING IN CROP CLASSIFICATION OF TEMPORAL MULTISPECTRAL SATELLITE IMAGE.” 2019. Web. 20 Aug 2019.

Vancouver:

Koppaka R. MACHINE LEARNING IN CROP CLASSIFICATION OF TEMPORAL MULTISPECTRAL SATELLITE IMAGE. [Internet] [Masters thesis]. San Jose State University; 2019. [cited 2019 Aug 20]. Available from: https://doi.org/10.31979/etd.z5tm-9zfg ; https://scholarworks.sjsu.edu/etd_projects/734.

Council of Science Editors:

Koppaka R. MACHINE LEARNING IN CROP CLASSIFICATION OF TEMPORAL MULTISPECTRAL SATELLITE IMAGE. [Masters Thesis]. San Jose State University; 2019. Available from: https://doi.org/10.31979/etd.z5tm-9zfg ; https://scholarworks.sjsu.edu/etd_projects/734


Portland State University

135. Landecker, Will. Interpretable Machine Learning and Sparse Coding for Computer Vision.

Degree: PhD, Computer Science, 2014, Portland State University

Machine learning offers many powerful tools for prediction. One of these tools, the binary classifier, is often considered a black box. Although its predictions… (more)

Subjects/Keywords: Machine learning  – Mathematical models; Computer vision  – Mathematical models; Compressed sensing (Telecommunication); Artificial Intelligence and Robotics; Numerical Analysis and Scientific Computing

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APA (6th Edition):

Landecker, W. (2014). Interpretable Machine Learning and Sparse Coding for Computer Vision. (Doctoral Dissertation). Portland State University. Retrieved from https://pdxscholar.library.pdx.edu/open_access_etds/1937

Chicago Manual of Style (16th Edition):

Landecker, Will. “Interpretable Machine Learning and Sparse Coding for Computer Vision.” 2014. Doctoral Dissertation, Portland State University. Accessed August 20, 2019. https://pdxscholar.library.pdx.edu/open_access_etds/1937.

MLA Handbook (7th Edition):

Landecker, Will. “Interpretable Machine Learning and Sparse Coding for Computer Vision.” 2014. Web. 20 Aug 2019.

Vancouver:

Landecker W. Interpretable Machine Learning and Sparse Coding for Computer Vision. [Internet] [Doctoral dissertation]. Portland State University; 2014. [cited 2019 Aug 20]. Available from: https://pdxscholar.library.pdx.edu/open_access_etds/1937.

Council of Science Editors:

Landecker W. Interpretable Machine Learning and Sparse Coding for Computer Vision. [Doctoral Dissertation]. Portland State University; 2014. Available from: https://pdxscholar.library.pdx.edu/open_access_etds/1937


Hong Kong University of Science and Technology

136. Chang, Hong. Semi-supervised distance metric learning.

Degree: 2006, Hong Kong University of Science and Technology

 Many machine learning and pattern recognition algorithms rely on a distance metric. Instead of choosing a metric manually, a more promising approach is to learn… (more)

Subjects/Keywords: Machine learning  – Mathematical models; Pattern recognition systems  – Mathematical models

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APA (6th Edition):

Chang, H. (2006). Semi-supervised distance metric learning. (Thesis). Hong Kong University of Science and Technology. Retrieved from https://doi.org/10.14711/thesis-b924396 ; http://repository.ust.hk/ir/bitstream/1783.1-2720/1/th_redirect.html

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, Hong. “Semi-supervised distance metric learning.” 2006. Thesis, Hong Kong University of Science and Technology. Accessed August 20, 2019. https://doi.org/10.14711/thesis-b924396 ; http://repository.ust.hk/ir/bitstream/1783.1-2720/1/th_redirect.html.

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

MLA Handbook (7th Edition):

Chang, Hong. “Semi-supervised distance metric learning.” 2006. Web. 20 Aug 2019.

Vancouver:

Chang H. Semi-supervised distance metric learning. [Internet] [Thesis]. Hong Kong University of Science and Technology; 2006. [cited 2019 Aug 20]. Available from: https://doi.org/10.14711/thesis-b924396 ; http://repository.ust.hk/ir/bitstream/1783.1-2720/1/th_redirect.html.

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

Council of Science Editors:

Chang H. Semi-supervised distance metric learning. [Thesis]. Hong Kong University of Science and Technology; 2006. Available from: https://doi.org/10.14711/thesis-b924396 ; http://repository.ust.hk/ir/bitstream/1783.1-2720/1/th_redirect.html

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


The Ohio State University

137. Lanka, Venkata Raghava Ravi Teja, Lanka. VEHICLE RESPONSE PREDICTION USING PHYSICAL AND MACHINE LEARNING MODELS.

Degree: MS, Mechanical Engineering, 2017, The Ohio State University

 With sporadic advancement in computer technology, transportation is moving towards autonomy. With rapid increase in production of highly automated vehicles (AVs), validation and safety of… (more)

Subjects/Keywords: Transportation; Mechanical Engineering; Engineering; Machine Learning, Longitudinal Car-Following Models, Extremely Randomized Trees, Random Forest, Hidden Markov Models, Validation

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APA (6th Edition):

Lanka, Venkata Raghava Ravi Teja, L. (2017). VEHICLE RESPONSE PREDICTION USING PHYSICAL AND MACHINE LEARNING MODELS. (Masters Thesis). The Ohio State University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=osu1511891682062084

Chicago Manual of Style (16th Edition):

Lanka, Venkata Raghava Ravi Teja, Lanka. “VEHICLE RESPONSE PREDICTION USING PHYSICAL AND MACHINE LEARNING MODELS.” 2017. Masters Thesis, The Ohio State University. Accessed August 20, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1511891682062084.

MLA Handbook (7th Edition):

Lanka, Venkata Raghava Ravi Teja, Lanka. “VEHICLE RESPONSE PREDICTION USING PHYSICAL AND MACHINE LEARNING MODELS.” 2017. Web. 20 Aug 2019.

Vancouver:

Lanka, Venkata Raghava Ravi Teja L. VEHICLE RESPONSE PREDICTION USING PHYSICAL AND MACHINE LEARNING MODELS. [Internet] [Masters thesis]. The Ohio State University; 2017. [cited 2019 Aug 20]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1511891682062084.

Council of Science Editors:

Lanka, Venkata Raghava Ravi Teja L. VEHICLE RESPONSE PREDICTION USING PHYSICAL AND MACHINE LEARNING MODELS. [Masters Thesis]. The Ohio State University; 2017. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1511891682062084

138. Sarkar, Dripta. Mathematical modeling and optimization of wave energy converters and arrays.

Degree: 2016, University College Dublin. School of Mathematical Sciences

The aim of this work is to develop methodologies and understand the dynamics of waveenergy energy converters (WECs) in some problems of practical interest. The… (more)

Subjects/Keywords: Array; Hydrodyamics; Machine learning; Optimization; Oyster; Wave energy; 0|Ocean wave power|xMathematical models.; #0|Energy conversion|xMathematical models.

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APA (6th Edition):

Sarkar, D. (2016). Mathematical modeling and optimization of wave energy converters and arrays. (Thesis). University College Dublin. School of Mathematical Sciences. Retrieved from http://hdl.handle.net/10197/7898

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):

Sarkar, Dripta. “Mathematical modeling and optimization of wave energy converters and arrays.” 2016. Thesis, University College Dublin. School of Mathematical Sciences. Accessed August 20, 2019. http://hdl.handle.net/10197/7898.

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

MLA Handbook (7th Edition):

Sarkar, Dripta. “Mathematical modeling and optimization of wave energy converters and arrays.” 2016. Web. 20 Aug 2019.

Vancouver:

Sarkar D. Mathematical modeling and optimization of wave energy converters and arrays. [Internet] [Thesis]. University College Dublin. School of Mathematical Sciences; 2016. [cited 2019 Aug 20]. Available from: http://hdl.handle.net/10197/7898.

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

Council of Science Editors:

Sarkar D. Mathematical modeling and optimization of wave energy converters and arrays. [Thesis]. University College Dublin. School of Mathematical Sciences; 2016. Available from: http://hdl.handle.net/10197/7898

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


Penn State University

139. Wang, Xingsheng. THE EFFECT OF SPATIAL SEGMENTATION ON SAFETY PERFORMANCE FUNCTION MODELING.

Degree: 2017, Penn State University

 Building predictive models called safety performance functions (SPFs) is important for the study of roadway safety. The first step in SPF modeling is roadway segmentation,… (more)

Subjects/Keywords: safety performance functions; roadway segmentation; machine learning; Negative Binominal models; coordinate-descent approach; Weighted Absolute Percentage Error; clustering; generalizability of models

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APA (6th Edition):

Wang, X. (2017). THE EFFECT OF SPATIAL SEGMENTATION ON SAFETY PERFORMANCE FUNCTION MODELING. (Thesis). Penn State University. Retrieved from https://etda.libraries.psu.edu/catalog/13766xxw15

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

Chicago Manual of Style (16th Edition):

Wang, Xingsheng. “THE EFFECT OF SPATIAL SEGMENTATION ON SAFETY PERFORMANCE FUNCTION MODELING.” 2017. Thesis, Penn State University. Accessed August 20, 2019. https://etda.libraries.psu.edu/catalog/13766xxw15.

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

MLA Handbook (7th Edition):

Wang, Xingsheng. “THE EFFECT OF SPATIAL SEGMENTATION ON SAFETY PERFORMANCE FUNCTION MODELING.” 2017. Web. 20 Aug 2019.

Vancouver:

Wang X. THE EFFECT OF SPATIAL SEGMENTATION ON SAFETY PERFORMANCE FUNCTION MODELING. [Internet] [Thesis]. Penn State University; 2017. [cited 2019 Aug 20]. Available from: https://etda.libraries.psu.edu/catalog/13766xxw15.

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

Council of Science Editors:

Wang X. THE EFFECT OF SPATIAL SEGMENTATION ON SAFETY PERFORMANCE FUNCTION MODELING. [Thesis]. Penn State University; 2017. Available from: https://etda.libraries.psu.edu/catalog/13766xxw15

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


University of Colorado

140. Azofeifa, Joseph Gaspare. Stochastic Modeling of RNA Polymerase Predicts Transcription Factor Activity.

Degree: PhD, 2017, University of Colorado

  Seventy-six percent of disease associated variants occur in non-genic sites of open chromatin suggesting that the regulation of gene expression plays a crucial role… (more)

Subjects/Keywords: genetics; gro-seq; hidden markov models; high throughput sequencing; machine learning; mixture models; Bioinformatics; Computer Sciences

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APA (6th Edition):

Azofeifa, J. G. (2017). Stochastic Modeling of RNA Polymerase Predicts Transcription Factor Activity. (Doctoral Dissertation). University of Colorado. Retrieved from https://scholar.colorado.edu/csci_gradetds/193

Chicago Manual of Style (16th Edition):

Azofeifa, Joseph Gaspare. “Stochastic Modeling of RNA Polymerase Predicts Transcription Factor Activity.” 2017. Doctoral Dissertation, University of Colorado. Accessed August 20, 2019. https://scholar.colorado.edu/csci_gradetds/193.

MLA Handbook (7th Edition):

Azofeifa, Joseph Gaspare. “Stochastic Modeling of RNA Polymerase Predicts Transcription Factor Activity.” 2017. Web. 20 Aug 2019.

Vancouver:

Azofeifa JG. Stochastic Modeling of RNA Polymerase Predicts Transcription Factor Activity. [Internet] [Doctoral dissertation]. University of Colorado; 2017. [cited 2019 Aug 20]. Available from: https://scholar.colorado.edu/csci_gradetds/193.

Council of Science Editors:

Azofeifa JG. Stochastic Modeling of RNA Polymerase Predicts Transcription Factor Activity. [Doctoral Dissertation]. University of Colorado; 2017. Available from: https://scholar.colorado.edu/csci_gradetds/193


University of Colorado

141. Azofeifa, Joseph Gaspare. Stochastic Modeling of RNA Polymerase Predicts Transcription Factor Activity.

Degree: PhD, 2017, University of Colorado

  Seventy-six percent of disease associated variants occur in non-genic sites of open chromatin suggesting that the regulation of gene expression plays a crucial role… (more)

Subjects/Keywords: genetics; GRO-seq; hidden Markov models; high throughput sequencing; machine learning; mixture models; Bioinformatics; Molecular Genetics

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

APA (6th Edition):

Azofeifa, J. G. (2017). Stochastic Modeling of RNA Polymerase Predicts Transcription Factor Activity. (Doctoral Dissertation). University of Colorado. Retrieved from https://scholar.colorado.edu/csci_gradetds/137

Chicago Manual of Style (16th Edition):

Azofeifa, Joseph Gaspare. “Stochastic Modeling of RNA Polymerase Predicts Transcription Factor Activity.” 2017. Doctoral Dissertation, University of Colorado. Accessed August 20, 2019. https://scholar.colorado.edu/csci_gradetds/137.

MLA Handbook (7th Edition):

Azofeifa, Joseph Gaspare. “Stochastic Modeling of RNA Polymerase Predicts Transcription Factor Activity.” 2017. Web. 20 Aug 2019.

Vancouver:

Azofeifa JG. Stochastic Modeling of RNA Polymerase Predicts Transcription Factor Activity. [Internet] [Doctoral dissertation]. University of Colorado; 2017. [cited 2019 Aug 20]. Available from: https://scholar.colorado.edu/csci_gradetds/137.

Council of Science Editors:

Azofeifa JG. Stochastic Modeling of RNA Polymerase Predicts Transcription Factor Activity. [Doctoral Dissertation]. University of Colorado; 2017. Available from: https://scholar.colorado.edu/csci_gradetds/137


Louisiana State University

142. Moscovich, Luis G. Learning discrete Hidden Markov Models from state distribution vectors.

Degree: PhD, Computer Sciences, 2004, Louisiana State University

 Hidden Markov Models (HMMs) are probabilistic models that have been widely applied to a number of fields since their inception in the late 1960’s. Computational… (more)

Subjects/Keywords: hidden Markov models; computational learning theory; machine learning

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APA (6th Edition):

Moscovich, L. G. (2004). Learning discrete Hidden Markov Models from state distribution vectors. (Doctoral Dissertation). Louisiana State University. Retrieved from etd-12212004-191037 ; https://digitalcommons.lsu.edu/gradschool_dissertations/4068

Chicago Manual of Style (16th Edition):

Moscovich, Luis G. “Learning discrete Hidden Markov Models from state distribution vectors.” 2004. Doctoral Dissertation, Louisiana State University. Accessed August 20, 2019. etd-12212004-191037 ; https://digitalcommons.lsu.edu/gradschool_dissertations/4068.

MLA Handbook (7th Edition):

Moscovich, Luis G. “Learning discrete Hidden Markov Models from state distribution vectors.” 2004. Web. 20 Aug 2019.

Vancouver:

Moscovich LG. Learning discrete Hidden Markov Models from state distribution vectors. [Internet] [Doctoral dissertation]. Louisiana State University; 2004. [cited 2019 Aug 20]. Available from: etd-12212004-191037 ; https://digitalcommons.lsu.edu/gradschool_dissertations/4068.

Council of Science Editors:

Moscovich LG. Learning discrete Hidden Markov Models from state distribution vectors. [Doctoral Dissertation]. Louisiana State University; 2004. Available from: etd-12212004-191037 ; https://digitalcommons.lsu.edu/gradschool_dissertations/4068


Ryerson University

143. Behzadfar, Hesaneh. Applying supervised learning algorithms on information derived from Social Network to enhance recommender systems.

Degree: 2014, Ryerson University

 The aim of this research is to show how social networks can be used for marketing purposes. This is implemented with the assistance of learning(more)

Subjects/Keywords: Supervised learning (Machine learning)  – Statistical methods; Online social networks; Marketing  – Mathematical models; Data mining  – Statistical methods; Facebook (Firm)

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

APA (6th Edition):

Behzadfar, H. (2014). Applying supervised learning algorithms on information derived from Social Network to enhance recommender systems. (Thesis). Ryerson University. Retrieved from https://digital.library.ryerson.ca/islandora/object/RULA%3A3285

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):

Behzadfar, Hesaneh. “Applying supervised learning algorithms on information derived from Social Network to enhance recommender systems.” 2014. Thesis, Ryerson University. Accessed August 20, 2019. https://digital.library.ryerson.ca/islandora/object/RULA%3A3285.

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

MLA Handbook (7th Edition):

Behzadfar, Hesaneh. “Applying supervised learning algorithms on information derived from Social Network to enhance recommender systems.” 2014. Web. 20 Aug 2019.

Vancouver:

Behzadfar H. Applying supervised learning algorithms on information derived from Social Network to enhance recommender systems. [Internet] [Thesis]. Ryerson University; 2014. [cited 2019 Aug 20]. Available from: https://digital.library.ryerson.ca/islandora/object/RULA%3A3285.

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

Council of Science Editors:

Behzadfar H. Applying supervised learning algorithms on information derived from Social Network to enhance recommender systems. [Thesis]. Ryerson University; 2014. Available from: https://digital.library.ryerson.ca/islandora/object/RULA%3A3285

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


University of Florida

144. Tarifi, Mohamad H. Foundations towards an Integrated Theory of Intelligence.

Degree: PhD, Computer Engineering - Computer and Information Science and Engineering, 2012, University of Florida

 This work outlines the beginning of a new attempt at forming an Integrated Theory of Intelligence.  A complete understanding of intelligence requires a multitude of… (more)

Subjects/Keywords: Algebra; Algorithms; Approximation; Dimensionality reduction; Learning disabilities; Machine learning; Mathematical vectors; Mathematics; Multilevel models; Neuroscience; geometric  – integrated

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APA (6th Edition):

Tarifi, M. H. (2012). Foundations towards an Integrated Theory of Intelligence. (Doctoral Dissertation). University of Florida. Retrieved from http://ufdc.ufl.edu/UFE0044666

Chicago Manual of Style (16th Edition):

Tarifi, Mohamad H. “Foundations towards an Integrated Theory of Intelligence.” 2012. Doctoral Dissertation, University of Florida. Accessed August 20, 2019. http://ufdc.ufl.edu/UFE0044666.

MLA Handbook (7th Edition):

Tarifi, Mohamad H. “Foundations towards an Integrated Theory of Intelligence.” 2012. Web. 20 Aug 2019.

Vancouver:

Tarifi MH. Foundations towards an Integrated Theory of Intelligence. [Internet] [Doctoral dissertation]. University of Florida; 2012. [cited 2019 Aug 20]. Available from: http://ufdc.ufl.edu/UFE0044666.

Council of Science Editors:

Tarifi MH. Foundations towards an Integrated Theory of Intelligence. [Doctoral Dissertation]. University of Florida; 2012. Available from: http://ufdc.ufl.edu/UFE0044666


Duke University

145. Gan, Zhe. Deep Generative Models for Vision and Language Intelligence .

Degree: 2018, Duke University

  Deep generative models have achieved tremendous success in recent years, with applications in various tasks involving vision and language intelligence. In this dissertation, I… (more)

Subjects/Keywords: Artificial intelligence; Electrical engineering; Computer science; deep generative models; deep learning; generative adversarial networks; machine learning; sigmoid belief networks; visual captioning

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APA (6th Edition):

Gan, Z. (2018). Deep Generative Models for Vision and Language Intelligence . (Thesis). Duke University. Retrieved from http://hdl.handle.net/10161/16810

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):

Gan, Zhe. “Deep Generative Models for Vision and Language Intelligence .” 2018. Thesis, Duke University. Accessed August 20, 2019. http://hdl.handle.net/10161/16810.

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

MLA Handbook (7th Edition):

Gan, Zhe. “Deep Generative Models for Vision and Language Intelligence .” 2018. Web. 20 Aug 2019.

Vancouver:

Gan Z. Deep Generative Models for Vision and Language Intelligence . [Internet] [Thesis]. Duke University; 2018. [cited 2019 Aug 20]. Available from: http://hdl.handle.net/10161/16810.

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

Council of Science Editors:

Gan Z. Deep Generative Models for Vision and Language Intelligence . [Thesis]. Duke University; 2018. Available from: http://hdl.handle.net/10161/16810

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


KTH

146. Pakdaman, Hesam. Updating the generator in PPGN-h with gradients flowing through the encoder.

Degree: Electrical Engineering and Computer Science (EECS), 2018, KTH

<em>The Generative Adversarial Network framework has shown success in implicitly modeling data distributions and is able to generate realistic samples. Its architecture is comprised… (more)

Subjects/Keywords: Computer Science; Computer Vision; Deep Learning; Machine Learning; Generative Adversarial Networks; GAN; Neural Networks; Generative models; Computer Sciences; Datavetenskap (datalogi)

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APA (6th Edition):

Pakdaman, H. (2018). Updating the generator in PPGN-h with gradients flowing through the encoder. (Thesis). KTH. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-224867

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):

Pakdaman, Hesam. “Updating the generator in PPGN-h with gradients flowing through the encoder.” 2018. Thesis, KTH. Accessed August 20, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-224867.

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

MLA Handbook (7th Edition):

Pakdaman, Hesam. “Updating the generator in PPGN-h with gradients flowing through the encoder.” 2018. Web. 20 Aug 2019.

Vancouver:

Pakdaman H. Updating the generator in PPGN-h with gradients flowing through the encoder. [Internet] [Thesis]. KTH; 2018. [cited 2019 Aug 20]. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-224867.

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

Council of Science Editors:

Pakdaman H. Updating the generator in PPGN-h with gradients flowing through the encoder. [Thesis]. KTH; 2018. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-224867

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


McMaster University

147. KHADEMI, MAHMOUD. Probabilistic Graphical Models for Prognosis and Diagnosis of Breast Cancer.

Degree: MSc, 2013, McMaster University

One in nine women is expected to be diagnosed with breast cancer during her life. In 2013, an estimated 23, 800 Canadian women will… (more)

Subjects/Keywords: Artificial Intelligence; Statistical Machine Learning; Probabilistic Graphical Models; Manifold Learning; Microarray Data; Breast Cancer; Other Computer Engineering; Other Computer Engineering

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APA (6th Edition):

KHADEMI, M. (2013). Probabilistic Graphical Models for Prognosis and Diagnosis of Breast Cancer. (Masters Thesis). McMaster University. Retrieved from http://hdl.handle.net/11375/13754

Chicago Manual of Style (16th Edition):

KHADEMI, MAHMOUD. “Probabilistic Graphical Models for Prognosis and Diagnosis of Breast Cancer.” 2013. Masters Thesis, McMaster University. Accessed August 20, 2019. http://hdl.handle.net/11375/13754.

MLA Handbook (7th Edition):

KHADEMI, MAHMOUD. “Probabilistic Graphical Models for Prognosis and Diagnosis of Breast Cancer.” 2013. Web. 20 Aug 2019.

Vancouver:

KHADEMI M. Probabilistic Graphical Models for Prognosis and Diagnosis of Breast Cancer. [Internet] [Masters thesis]. McMaster University; 2013. [cited 2019 Aug 20]. Available from: http://hdl.handle.net/11375/13754.

Council of Science Editors:

KHADEMI M. Probabilistic Graphical Models for Prognosis and Diagnosis of Breast Cancer. [Masters Thesis]. McMaster University; 2013. Available from: http://hdl.handle.net/11375/13754


University of Kansas

148. St.Amand, Joseph. Learning to Measure: Distance Metric Learning with Structured Sparsity.

Degree: PhD, Electrical Engineering & Computer Science, 2018, University of Kansas

 Many important machine learning and data mining algorithms rely on a measure to provide a notion of distance or dissimilarity. Naive metrics such as the… (more)

Subjects/Keywords: Artificial intelligence; Computer science; Information science; classification; machine learning; metric learning; regression; sparse models; statistical modeling

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APA (6th Edition):

St.Amand, J. (2018). Learning to Measure: Distance Metric Learning with Structured Sparsity. (Doctoral Dissertation). University of Kansas. Retrieved from http://hdl.handle.net/1808/27811

Chicago Manual of Style (16th Edition):

St.Amand, Joseph. “Learning to Measure: Distance Metric Learning with Structured Sparsity.” 2018. Doctoral Dissertation, University of Kansas. Accessed August 20, 2019. http://hdl.handle.net/1808/27811.

MLA Handbook (7th Edition):

St.Amand, Joseph. “Learning to Measure: Distance Metric Learning with Structured Sparsity.” 2018. Web. 20 Aug 2019.

Vancouver:

St.Amand J. Learning to Measure: Distance Metric Learning with Structured Sparsity. [Internet] [Doctoral dissertation]. University of Kansas; 2018. [cited 2019 Aug 20]. Available from: http://hdl.handle.net/1808/27811.

Council of Science Editors:

St.Amand J. Learning to Measure: Distance Metric Learning with Structured Sparsity. [Doctoral Dissertation]. University of Kansas; 2018. Available from: http://hdl.handle.net/1808/27811


University of Michigan

149. Meng, Zhaoshi. Distributed Learning, Prediction and Detection in Probabilistic Graphs.

Degree: PhD, Electrical Engineering: Systems, 2014, University of Michigan

 Critical to high-dimensional statistical estimation is to exploit the structure in the data distribution. Probabilistic graphical models provide an efficient framework for representing complex joint… (more)

Subjects/Keywords: probabilistic graphical models; machine learning; high-dimensional statistics; statistical estimation; distributed learning and estimation; Computer Science; Engineering

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APA (6th Edition):

Meng, Z. (2014). Distributed Learning, Prediction and Detection in Probabilistic Graphs. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/110499

Chicago Manual of Style (16th Edition):

Meng, Zhaoshi. “Distributed Learning, Prediction and Detection in Probabilistic Graphs.” 2014. Doctoral Dissertation, University of Michigan. Accessed August 20, 2019. http://hdl.handle.net/2027.42/110499.

MLA Handbook (7th Edition):

Meng, Zhaoshi. “Distributed Learning, Prediction and Detection in Probabilistic Graphs.” 2014. Web. 20 Aug 2019.

Vancouver:

Meng Z. Distributed Learning, Prediction and Detection in Probabilistic Graphs. [Internet] [Doctoral dissertation]. University of Michigan; 2014. [cited 2019 Aug 20]. Available from: http://hdl.handle.net/2027.42/110499.

Council of Science Editors:

Meng Z. Distributed Learning, Prediction and Detection in Probabilistic Graphs. [Doctoral Dissertation]. University of Michigan; 2014. Available from: http://hdl.handle.net/2027.42/110499


Northeastern University

150. Shao, Ming. Efficient transfer feature learning and its applications on social media.

Degree: PhD, Department of Electrical and Computer Engineering, 2016, Northeastern University

 In the era of social media, more and more social characteristics are conveyed by multimedia, i.e., images, videos, audios, and webpages with rich media information.… (more)

Subjects/Keywords: domain adaptation; kinship verification; low-rank matrix analysis; transfer learning; Computer vision; Machine learning; Social media; Biometric identification; Mathematical models; Matrices

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APA (6th Edition):

Shao, M. (2016). Efficient transfer feature learning and its applications on social media. (Doctoral Dissertation). Northeastern University. Retrieved from http://hdl.handle.net/2047/D20213068

Chicago Manual of Style (16th Edition):

Shao, Ming. “Efficient transfer feature learning and its applications on social media.” 2016. Doctoral Dissertation, Northeastern University. Accessed August 20, 2019. http://hdl.handle.net/2047/D20213068.

MLA Handbook (7th Edition):

Shao, Ming. “Efficient transfer feature learning and its applications on social media.” 2016. Web. 20 Aug 2019.

Vancouver:

Shao M. Efficient transfer feature learning and its applications on social media. [Internet] [Doctoral dissertation]. Northeastern University; 2016. [cited 2019 Aug 20]. Available from: http://hdl.handle.net/2047/D20213068.

Council of Science Editors:

Shao M. Efficient transfer feature learning and its applications on social media. [Doctoral Dissertation]. Northeastern University; 2016. Available from: http://hdl.handle.net/2047/D20213068

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