Advanced search options

Advanced Search Options 🞨

Browse by author name (“Author name starts with…”).

Find ETDs with:

in
/  
in
/  
in
/  
in

Written in Published in Earliest date Latest date

Sorted by

Results per page:

Sorted by: relevance · author · university · dateNew search

You searched for +publisher:"Vanderbilt University" +contributor:("Dr. Douglas H. Fisher"). Showing records 1 – 4 of 4 total matches.

Search Limiters

Last 2 Years | English Only

No search limiters apply to these results.

▼ Search Limiters


Vanderbilt University

1. Sivley, Robert Michael. Clustering Rare Event Features to Increase Statistical Power.

Degree: MS, Computer Science, 2013, Vanderbilt University

 Rare genetic variation has been put forward as a major contributor to the development of disease; however, it is inherently difficult to associate rare variants… (more)

Subjects/Keywords: power; statistics; rvclust; clustering; rare event; rare variant

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Sivley, R. M. (2013). Clustering Rare Event Features to Increase Statistical Power. (Thesis). Vanderbilt University. Retrieved from http://hdl.handle.net/1803/12064

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):

Sivley, Robert Michael. “Clustering Rare Event Features to Increase Statistical Power.” 2013. Thesis, Vanderbilt University. Accessed January 15, 2021. http://hdl.handle.net/1803/12064.

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

MLA Handbook (7th Edition):

Sivley, Robert Michael. “Clustering Rare Event Features to Increase Statistical Power.” 2013. Web. 15 Jan 2021.

Vancouver:

Sivley RM. Clustering Rare Event Features to Increase Statistical Power. [Internet] [Thesis]. Vanderbilt University; 2013. [cited 2021 Jan 15]. Available from: http://hdl.handle.net/1803/12064.

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

Council of Science Editors:

Sivley RM. Clustering Rare Event Features to Increase Statistical Power. [Thesis]. Vanderbilt University; 2013. Available from: http://hdl.handle.net/1803/12064

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


Vanderbilt University

2. Basu, Satabdi. Fostering Synergistic Learning of Computational Thinking and Middle School Science in Computer-based Intelligent Learning Environments.

Degree: PhD, Computer Science, 2016, Vanderbilt University

 Recent advances in computing are transforming our lives at an astonishing pace. Computational Thinking (CT) is a term used to describe the representational practices and… (more)

Subjects/Keywords: Adaptive scaffolding; Agent based Modeling; Learning by Modeling; Science Education; Computational Thinking; Open ended Learning Environments; Learning Analytics

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Basu, S. (2016). Fostering Synergistic Learning of Computational Thinking and Middle School Science in Computer-based Intelligent Learning Environments. (Doctoral Dissertation). Vanderbilt University. Retrieved from http://hdl.handle.net/1803/11338

Chicago Manual of Style (16th Edition):

Basu, Satabdi. “Fostering Synergistic Learning of Computational Thinking and Middle School Science in Computer-based Intelligent Learning Environments.” 2016. Doctoral Dissertation, Vanderbilt University. Accessed January 15, 2021. http://hdl.handle.net/1803/11338.

MLA Handbook (7th Edition):

Basu, Satabdi. “Fostering Synergistic Learning of Computational Thinking and Middle School Science in Computer-based Intelligent Learning Environments.” 2016. Web. 15 Jan 2021.

Vancouver:

Basu S. Fostering Synergistic Learning of Computational Thinking and Middle School Science in Computer-based Intelligent Learning Environments. [Internet] [Doctoral dissertation]. Vanderbilt University; 2016. [cited 2021 Jan 15]. Available from: http://hdl.handle.net/1803/11338.

Council of Science Editors:

Basu S. Fostering Synergistic Learning of Computational Thinking and Middle School Science in Computer-based Intelligent Learning Environments. [Doctoral Dissertation]. Vanderbilt University; 2016. Available from: http://hdl.handle.net/1803/11338


Vanderbilt University

3. Sen, Sayan Dev. An intelligent and unified framework for multiple robot and human coalition formation.

Degree: PhD, Computer Science, 2015, Vanderbilt University

 Robotic systems have proven effective with recent deployments of unmanned robots in numerous missions. Teaming multiple agents requires efficient coalition formation, which is an NP-complete… (more)

Subjects/Keywords: Multi-robot systems; Coalition formation; Swarm Intelligence; Optimization

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Sen, S. D. (2015). An intelligent and unified framework for multiple robot and human coalition formation. (Doctoral Dissertation). Vanderbilt University. Retrieved from http://hdl.handle.net/1803/11235

Chicago Manual of Style (16th Edition):

Sen, Sayan Dev. “An intelligent and unified framework for multiple robot and human coalition formation.” 2015. Doctoral Dissertation, Vanderbilt University. Accessed January 15, 2021. http://hdl.handle.net/1803/11235.

MLA Handbook (7th Edition):

Sen, Sayan Dev. “An intelligent and unified framework for multiple robot and human coalition formation.” 2015. Web. 15 Jan 2021.

Vancouver:

Sen SD. An intelligent and unified framework for multiple robot and human coalition formation. [Internet] [Doctoral dissertation]. Vanderbilt University; 2015. [cited 2021 Jan 15]. Available from: http://hdl.handle.net/1803/11235.

Council of Science Editors:

Sen SD. An intelligent and unified framework for multiple robot and human coalition formation. [Doctoral Dissertation]. Vanderbilt University; 2015. Available from: http://hdl.handle.net/1803/11235


Vanderbilt University

4. Freedman, Sanford Tory. Human-Inspired Forgetting for Robotic Systems.

Degree: PhD, Computer Science, 2010, Vanderbilt University

  – PLEASE NOTE THAT THE ATTACHED .7z FILE CONTAINING VIDEOS WILL NEED TO BE EXTRACTED AND BURNED TO A DVD IN ORDER TO BE VIEWED. –… (more)

Subjects/Keywords: Human-Inspired Forgetting; Mobile Robot

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Freedman, S. T. (2010). Human-Inspired Forgetting for Robotic Systems. (Doctoral Dissertation). Vanderbilt University. Retrieved from http://hdl.handle.net/1803/15011

Chicago Manual of Style (16th Edition):

Freedman, Sanford Tory. “Human-Inspired Forgetting for Robotic Systems.” 2010. Doctoral Dissertation, Vanderbilt University. Accessed January 15, 2021. http://hdl.handle.net/1803/15011.

MLA Handbook (7th Edition):

Freedman, Sanford Tory. “Human-Inspired Forgetting for Robotic Systems.” 2010. Web. 15 Jan 2021.

Vancouver:

Freedman ST. Human-Inspired Forgetting for Robotic Systems. [Internet] [Doctoral dissertation]. Vanderbilt University; 2010. [cited 2021 Jan 15]. Available from: http://hdl.handle.net/1803/15011.

Council of Science Editors:

Freedman ST. Human-Inspired Forgetting for Robotic Systems. [Doctoral Dissertation]. Vanderbilt University; 2010. Available from: http://hdl.handle.net/1803/15011

.