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Dates: 2010 – 2014

You searched for +publisher:"University of Texas – Austin" +contributor:("Mooney, Raymond"). Showing records 1 – 25 of 25 total matches.

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University of Texas – Austin

1. Reisinger, Joseph Simon. Latent variable models of distributional lexical semantics.

Degree: Computer Sciences, 2012, University of Texas – Austin

 In order to respond to increasing demand for natural language interfaces – and provide meaningful insight into user query intent – fast, scalable lexical semantic models with… (more)

Subjects/Keywords: Lexical semantics; Natural language processing; Machine learning

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

APA (6th Edition):

Reisinger, J. S. (2012). Latent variable models of distributional lexical semantics. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/26889

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):

Reisinger, Joseph Simon. “Latent variable models of distributional lexical semantics.” 2012. Thesis, University of Texas – Austin. Accessed June 25, 2019. http://hdl.handle.net/2152/26889.

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

MLA Handbook (7th Edition):

Reisinger, Joseph Simon. “Latent variable models of distributional lexical semantics.” 2012. Web. 25 Jun 2019.

Vancouver:

Reisinger JS. Latent variable models of distributional lexical semantics. [Internet] [Thesis]. University of Texas – Austin; 2012. [cited 2019 Jun 25]. Available from: http://hdl.handle.net/2152/26889.

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

Council of Science Editors:

Reisinger JS. Latent variable models of distributional lexical semantics. [Thesis]. University of Texas – Austin; 2012. Available from: http://hdl.handle.net/2152/26889

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


University of Texas – Austin

2. Ge, Ruifang. Learning for semantic parsing using statistical syntactic parsing techniques.

Degree: Computer Sciences, 2010, University of Texas – Austin

 Natural language understanding is a sub-field of natural language processing, which builds automated systems to understand natural language. It is such an ambitious task that… (more)

Subjects/Keywords: Semantic parsing; Statistical syntactic parsing

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APA (6th Edition):

Ge, R. (2010). Learning for semantic parsing using statistical syntactic parsing techniques. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/26599

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):

Ge, Ruifang. “Learning for semantic parsing using statistical syntactic parsing techniques.” 2010. Thesis, University of Texas – Austin. Accessed June 25, 2019. http://hdl.handle.net/2152/26599.

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

MLA Handbook (7th Edition):

Ge, Ruifang. “Learning for semantic parsing using statistical syntactic parsing techniques.” 2010. Web. 25 Jun 2019.

Vancouver:

Ge R. Learning for semantic parsing using statistical syntactic parsing techniques. [Internet] [Thesis]. University of Texas – Austin; 2010. [cited 2019 Jun 25]. Available from: http://hdl.handle.net/2152/26599.

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

Council of Science Editors:

Ge R. Learning for semantic parsing using statistical syntactic parsing techniques. [Thesis]. University of Texas – Austin; 2010. Available from: http://hdl.handle.net/2152/26599

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


University of Texas – Austin

3. Kuhlmann, Gregory John. Automated domain analysis and transfer learning in general game playing.

Degree: Computer Sciences, 2010, University of Texas – Austin

 Creating programs that can play games such as chess, checkers, and backgammon, at a high level has long been a challenge and benchmark for AI.… (more)

Subjects/Keywords: Games; Machine learning; Knowledge transfer; Reinforcement learning; General game playing; Computer games; Artificial Intelligence; AI

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APA (6th Edition):

Kuhlmann, G. J. (2010). Automated domain analysis and transfer learning in general game playing. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/ETD-UT-2010-08-1975

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):

Kuhlmann, Gregory John. “Automated domain analysis and transfer learning in general game playing.” 2010. Thesis, University of Texas – Austin. Accessed June 25, 2019. http://hdl.handle.net/2152/ETD-UT-2010-08-1975.

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

MLA Handbook (7th Edition):

Kuhlmann, Gregory John. “Automated domain analysis and transfer learning in general game playing.” 2010. Web. 25 Jun 2019.

Vancouver:

Kuhlmann GJ. Automated domain analysis and transfer learning in general game playing. [Internet] [Thesis]. University of Texas – Austin; 2010. [cited 2019 Jun 25]. Available from: http://hdl.handle.net/2152/ETD-UT-2010-08-1975.

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

Council of Science Editors:

Kuhlmann GJ. Automated domain analysis and transfer learning in general game playing. [Thesis]. University of Texas – Austin; 2010. Available from: http://hdl.handle.net/2152/ETD-UT-2010-08-1975

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


University of Texas – Austin

4. Krumpelman, Chase Serhur. Overlapping clustering.

Degree: Electrical and Computer Engineering, 2010, University of Texas – Austin

 Analysis of large collections of data has become inescapable in many areas of scientific and commercial endeavor. As the size and dimensionality of these collections… (more)

Subjects/Keywords: Clustering algorithms

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APA (6th Edition):

Krumpelman, C. S. (2010). Overlapping clustering. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/ETD-UT-2010-08-2022

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):

Krumpelman, Chase Serhur. “Overlapping clustering.” 2010. Thesis, University of Texas – Austin. Accessed June 25, 2019. http://hdl.handle.net/2152/ETD-UT-2010-08-2022.

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

MLA Handbook (7th Edition):

Krumpelman, Chase Serhur. “Overlapping clustering.” 2010. Web. 25 Jun 2019.

Vancouver:

Krumpelman CS. Overlapping clustering. [Internet] [Thesis]. University of Texas – Austin; 2010. [cited 2019 Jun 25]. Available from: http://hdl.handle.net/2152/ETD-UT-2010-08-2022.

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

Council of Science Editors:

Krumpelman CS. Overlapping clustering. [Thesis]. University of Texas – Austin; 2010. Available from: http://hdl.handle.net/2152/ETD-UT-2010-08-2022

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


University of Texas – Austin

5. Huynh, Tuyen Ngoc. Improving the accuracy and scalability of discriminative learning methods for Markov logic networks.

Degree: Computer Sciences, 2011, University of Texas – Austin

 Many real-world problems involve data that both have complex structures and uncertainty. Statistical relational learning (SRL) is an emerging area of research that addresses the… (more)

Subjects/Keywords: Markov logic networks; Statistical relational learning; Structured prediction; Logic networks; Artificial intelligence; Machine learning

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

APA (6th Edition):

Huynh, T. N. (2011). Improving the accuracy and scalability of discriminative learning methods for Markov logic networks. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/ETD-UT-2011-05-3436

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):

Huynh, Tuyen Ngoc. “Improving the accuracy and scalability of discriminative learning methods for Markov logic networks.” 2011. Thesis, University of Texas – Austin. Accessed June 25, 2019. http://hdl.handle.net/2152/ETD-UT-2011-05-3436.

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

MLA Handbook (7th Edition):

Huynh, Tuyen Ngoc. “Improving the accuracy and scalability of discriminative learning methods for Markov logic networks.” 2011. Web. 25 Jun 2019.

Vancouver:

Huynh TN. Improving the accuracy and scalability of discriminative learning methods for Markov logic networks. [Internet] [Thesis]. University of Texas – Austin; 2011. [cited 2019 Jun 25]. Available from: http://hdl.handle.net/2152/ETD-UT-2011-05-3436.

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

Council of Science Editors:

Huynh TN. Improving the accuracy and scalability of discriminative learning methods for Markov logic networks. [Thesis]. University of Texas – Austin; 2011. Available from: http://hdl.handle.net/2152/ETD-UT-2011-05-3436

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


University of Texas – Austin

6. Jong, Nicholas K. Structured exploration for reinforcement learning.

Degree: Computer Sciences, 2010, University of Texas – Austin

 Reinforcement Learning (RL) offers a promising approach towards achieving the dream of autonomous agents that can behave intelligently in the real world. Instead of requiring… (more)

Subjects/Keywords: Reinforcement learning; Machine learning

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APA (6th Edition):

Jong, N. K. (2010). Structured exploration for reinforcement learning. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/ETD-UT-2010-12-2448

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):

Jong, Nicholas K. “Structured exploration for reinforcement learning.” 2010. Thesis, University of Texas – Austin. Accessed June 25, 2019. http://hdl.handle.net/2152/ETD-UT-2010-12-2448.

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

MLA Handbook (7th Edition):

Jong, Nicholas K. “Structured exploration for reinforcement learning.” 2010. Web. 25 Jun 2019.

Vancouver:

Jong NK. Structured exploration for reinforcement learning. [Internet] [Thesis]. University of Texas – Austin; 2010. [cited 2019 Jun 25]. Available from: http://hdl.handle.net/2152/ETD-UT-2010-12-2448.

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

Council of Science Editors:

Jong NK. Structured exploration for reinforcement learning. [Thesis]. University of Texas – Austin; 2010. Available from: http://hdl.handle.net/2152/ETD-UT-2010-12-2448

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


University of Texas – Austin

7. Mugan, Jonathan William. Autonomous qualitative learning of distinctions and actions in a developing agent.

Degree: Computer Sciences, 2010, University of Texas – Austin

 How can an agent bootstrap up from a pixel-level representation to autonomously learn high-level states and actions using only domain general knowledge? This thesis attacks… (more)

Subjects/Keywords: Artificial intelligence; Robotics; Machine learning; Reinforcement learning; Discretization; Qualitative learning

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APA (6th Edition):

Mugan, J. W. (2010). Autonomous qualitative learning of distinctions and actions in a developing agent. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/ETD-UT-2010-08-1726

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):

Mugan, Jonathan William. “Autonomous qualitative learning of distinctions and actions in a developing agent.” 2010. Thesis, University of Texas – Austin. Accessed June 25, 2019. http://hdl.handle.net/2152/ETD-UT-2010-08-1726.

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

MLA Handbook (7th Edition):

Mugan, Jonathan William. “Autonomous qualitative learning of distinctions and actions in a developing agent.” 2010. Web. 25 Jun 2019.

Vancouver:

Mugan JW. Autonomous qualitative learning of distinctions and actions in a developing agent. [Internet] [Thesis]. University of Texas – Austin; 2010. [cited 2019 Jun 25]. Available from: http://hdl.handle.net/2152/ETD-UT-2010-08-1726.

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

Council of Science Editors:

Mugan JW. Autonomous qualitative learning of distinctions and actions in a developing agent. [Thesis]. University of Texas – Austin; 2010. Available from: http://hdl.handle.net/2152/ETD-UT-2010-08-1726

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


University of Texas – Austin

8. Cho, Tae Won, 1978-. Enabling information-centric networking : architecture, protocols, and applications.

Degree: Computer Sciences, 2010, University of Texas – Austin

 As the Internet is becoming information-centric, network services increasingly demand scalable and efficient communication of information between a multitude of information producers and large groups… (more)

Subjects/Keywords: Information-centric networking; Network protocols; Social networks; Multicast; Networks; Proximity measures; Link prediction

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APA (6th Edition):

Cho, Tae Won, 1. (2010). Enabling information-centric networking : architecture, protocols, and applications. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/ETD-UT-2010-08-1765

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):

Cho, Tae Won, 1978-. “Enabling information-centric networking : architecture, protocols, and applications.” 2010. Thesis, University of Texas – Austin. Accessed June 25, 2019. http://hdl.handle.net/2152/ETD-UT-2010-08-1765.

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

MLA Handbook (7th Edition):

Cho, Tae Won, 1978-. “Enabling information-centric networking : architecture, protocols, and applications.” 2010. Web. 25 Jun 2019.

Vancouver:

Cho, Tae Won 1. Enabling information-centric networking : architecture, protocols, and applications. [Internet] [Thesis]. University of Texas – Austin; 2010. [cited 2019 Jun 25]. Available from: http://hdl.handle.net/2152/ETD-UT-2010-08-1765.

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

Council of Science Editors:

Cho, Tae Won 1. Enabling information-centric networking : architecture, protocols, and applications. [Thesis]. University of Texas – Austin; 2010. Available from: http://hdl.handle.net/2152/ETD-UT-2010-08-1765

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


University of Texas – Austin

9. Ramakrishnan, Smriti Rajan. A systems approach to computational protein identification.

Degree: Computer Sciences, 2010, University of Texas – Austin

 Proteomics is the science of understanding the dynamic protein content of an organism's cells (its proteome), which is one of the largest current challenges in… (more)

Subjects/Keywords: Computational biology; Bioinformatics; Integrative statistical data analysis; Computational proteomics; Systems biology; Database indexing

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APA (6th Edition):

Ramakrishnan, S. R. (2010). A systems approach to computational protein identification. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/ETD-UT-2010-05-1036

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):

Ramakrishnan, Smriti Rajan. “A systems approach to computational protein identification.” 2010. Thesis, University of Texas – Austin. Accessed June 25, 2019. http://hdl.handle.net/2152/ETD-UT-2010-05-1036.

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

MLA Handbook (7th Edition):

Ramakrishnan, Smriti Rajan. “A systems approach to computational protein identification.” 2010. Web. 25 Jun 2019.

Vancouver:

Ramakrishnan SR. A systems approach to computational protein identification. [Internet] [Thesis]. University of Texas – Austin; 2010. [cited 2019 Jun 25]. Available from: http://hdl.handle.net/2152/ETD-UT-2010-05-1036.

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

Council of Science Editors:

Ramakrishnan SR. A systems approach to computational protein identification. [Thesis]. University of Texas – Austin; 2010. Available from: http://hdl.handle.net/2152/ETD-UT-2010-05-1036

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


University of Texas – Austin

10. Vijayanarasimhan, Sudheendra. Active visual category learning.

Degree: Computer Sciences, 2011, University of Texas – Austin

 Visual recognition research develops algorithms and representations to autonomously recognize visual entities such as objects, actions, and attributes. The traditional protocol involves manually collecting training… (more)

Subjects/Keywords: Artificial intelligence; Active learning; Object recognition; Object detection; Cost-sensitive learning; Multi-level learning; Budgeted learning; Large-scale active learning; Live learning; Machine learning; Visual recognition system

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APA (6th Edition):

Vijayanarasimhan, S. (2011). Active visual category learning. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/ETD-UT-2011-05-3014

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):

Vijayanarasimhan, Sudheendra. “Active visual category learning.” 2011. Thesis, University of Texas – Austin. Accessed June 25, 2019. http://hdl.handle.net/2152/ETD-UT-2011-05-3014.

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

MLA Handbook (7th Edition):

Vijayanarasimhan, Sudheendra. “Active visual category learning.” 2011. Web. 25 Jun 2019.

Vancouver:

Vijayanarasimhan S. Active visual category learning. [Internet] [Thesis]. University of Texas – Austin; 2011. [cited 2019 Jun 25]. Available from: http://hdl.handle.net/2152/ETD-UT-2011-05-3014.

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

Council of Science Editors:

Vijayanarasimhan S. Active visual category learning. [Thesis]. University of Texas – Austin; 2011. Available from: http://hdl.handle.net/2152/ETD-UT-2011-05-3014

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


University of Texas – Austin

11. Kong, Danxia. Collaborative information acquisition.

Degree: Information, Risk, and Operations Management (IROM), 2011, University of Texas – Austin

 Increasingly, predictive models are used to support routine business de- cisions and are integral to the strategic competitive business strategies for a wide range of… (more)

Subjects/Keywords: Active information acquisition; Active learning; Multiple-tasks active learning; Decision-centric active learning; Active machine learning

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APA (6th Edition):

Kong, D. (2011). Collaborative information acquisition. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/ETD-UT-2011-12-4678

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):

Kong, Danxia. “Collaborative information acquisition.” 2011. Thesis, University of Texas – Austin. Accessed June 25, 2019. http://hdl.handle.net/2152/ETD-UT-2011-12-4678.

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

MLA Handbook (7th Edition):

Kong, Danxia. “Collaborative information acquisition.” 2011. Web. 25 Jun 2019.

Vancouver:

Kong D. Collaborative information acquisition. [Internet] [Thesis]. University of Texas – Austin; 2011. [cited 2019 Jun 25]. Available from: http://hdl.handle.net/2152/ETD-UT-2011-12-4678.

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

Council of Science Editors:

Kong D. Collaborative information acquisition. [Thesis]. University of Texas – Austin; 2011. Available from: http://hdl.handle.net/2152/ETD-UT-2011-12-4678

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


University of Texas – Austin

12. Grasemann, Hans Ulrich. A computational model of language pathology in schizophrenia.

Degree: Computer Sciences, 2010, University of Texas – Austin

 No current laboratory test can reliably identify patients with schizophrenia. Instead, key symptoms are observed via language, including derailment, where patients cannot follow a coherent… (more)

Subjects/Keywords: Schizophrenia; Neural networks; Connectionist; Natural language processing; Psychopathology

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APA (6th Edition):

Grasemann, H. U. (2010). A computational model of language pathology in schizophrenia. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/ETD-UT-2010-12-2589

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):

Grasemann, Hans Ulrich. “A computational model of language pathology in schizophrenia.” 2010. Thesis, University of Texas – Austin. Accessed June 25, 2019. http://hdl.handle.net/2152/ETD-UT-2010-12-2589.

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

MLA Handbook (7th Edition):

Grasemann, Hans Ulrich. “A computational model of language pathology in schizophrenia.” 2010. Web. 25 Jun 2019.

Vancouver:

Grasemann HU. A computational model of language pathology in schizophrenia. [Internet] [Thesis]. University of Texas – Austin; 2010. [cited 2019 Jun 25]. Available from: http://hdl.handle.net/2152/ETD-UT-2010-12-2589.

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

Council of Science Editors:

Grasemann HU. A computational model of language pathology in schizophrenia. [Thesis]. University of Texas – Austin; 2010. Available from: http://hdl.handle.net/2152/ETD-UT-2010-12-2589

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

13. Kim, Joo Hyun, active 2013. Grounded language learning models for ambiguous supervision.

Degree: Computer Sciences, 2013, University of Texas – Austin

 Communicating with natural language interfaces is a long-standing, ultimate goal for artificial intelligence (AI) agents to pursue, eventually. One core issue toward this goal is… (more)

Subjects/Keywords: Grounded language learning; Semantic parsing; Learning from ambiguous supervision; Probabilistic alignment; Natural language processing

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APA (6th Edition):

Kim, Joo Hyun, a. 2. (2013). Grounded language learning models for ambiguous supervision. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/22986

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):

Kim, Joo Hyun, active 2013. “Grounded language learning models for ambiguous supervision.” 2013. Thesis, University of Texas – Austin. Accessed June 25, 2019. http://hdl.handle.net/2152/22986.

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

MLA Handbook (7th Edition):

Kim, Joo Hyun, active 2013. “Grounded language learning models for ambiguous supervision.” 2013. Web. 25 Jun 2019.

Vancouver:

Kim, Joo Hyun a2. Grounded language learning models for ambiguous supervision. [Internet] [Thesis]. University of Texas – Austin; 2013. [cited 2019 Jun 25]. Available from: http://hdl.handle.net/2152/22986.

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

Council of Science Editors:

Kim, Joo Hyun a2. Grounded language learning models for ambiguous supervision. [Thesis]. University of Texas – Austin; 2013. Available from: http://hdl.handle.net/2152/22986

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

14. Acharya, Ayan. Combining classifier and cluster ensembles for semi-supervised and transfer learning.

Degree: Electrical and Computer Engineering, 2012, University of Texas – Austin

 Unsupervised models can provide supplementary soft constraints to help classify new, "target" data since similar instances in the target set are more likely to share… (more)

Subjects/Keywords: Ensemble; Classification; Clustering; Semi-supervised learning; Transfer learning; Alternating minimization; Privacy

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APA (6th Edition):

Acharya, A. (2012). Combining classifier and cluster ensembles for semi-supervised and transfer learning. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/ETD-UT-2012-05-5086

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):

Acharya, Ayan. “Combining classifier and cluster ensembles for semi-supervised and transfer learning.” 2012. Thesis, University of Texas – Austin. Accessed June 25, 2019. http://hdl.handle.net/2152/ETD-UT-2012-05-5086.

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

MLA Handbook (7th Edition):

Acharya, Ayan. “Combining classifier and cluster ensembles for semi-supervised and transfer learning.” 2012. Web. 25 Jun 2019.

Vancouver:

Acharya A. Combining classifier and cluster ensembles for semi-supervised and transfer learning. [Internet] [Thesis]. University of Texas – Austin; 2012. [cited 2019 Jun 25]. Available from: http://hdl.handle.net/2152/ETD-UT-2012-05-5086.

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

Council of Science Editors:

Acharya A. Combining classifier and cluster ensembles for semi-supervised and transfer learning. [Thesis]. University of Texas – Austin; 2012. Available from: http://hdl.handle.net/2152/ETD-UT-2012-05-5086

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

15. Chakraborty, Doran. Sample efficient multiagent learning in the presence of Markovian agents.

Degree: Computer Sciences, 2012, University of Texas – Austin

 The problem of multiagent learning (or MAL) is concerned with the study of how agents can learn and adapt in the presence of other agents… (more)

Subjects/Keywords: Artificial intelligence; Multiagent learning

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APA (6th Edition):

Chakraborty, D. (2012). Sample efficient multiagent learning in the presence of Markovian agents. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/19459

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):

Chakraborty, Doran. “Sample efficient multiagent learning in the presence of Markovian agents.” 2012. Thesis, University of Texas – Austin. Accessed June 25, 2019. http://hdl.handle.net/2152/19459.

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

MLA Handbook (7th Edition):

Chakraborty, Doran. “Sample efficient multiagent learning in the presence of Markovian agents.” 2012. Web. 25 Jun 2019.

Vancouver:

Chakraborty D. Sample efficient multiagent learning in the presence of Markovian agents. [Internet] [Thesis]. University of Texas – Austin; 2012. [cited 2019 Jun 25]. Available from: http://hdl.handle.net/2152/19459.

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

Council of Science Editors:

Chakraborty D. Sample efficient multiagent learning in the presence of Markovian agents. [Thesis]. University of Texas – Austin; 2012. Available from: http://hdl.handle.net/2152/19459

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

16. Hester, Todd. Texplore : temporal difference reinforcement learning for robots and time-constrained domains: Temporal difference reinforcement learning for robots and time-constrained domains.

Degree: Computer Sciences, 2012, University of Texas – Austin

 Robots have the potential to solve many problems in society, because of their ability to work in dangerous places doing necessary jobs that no one… (more)

Subjects/Keywords: Reinforcement learning; Robotics; Machine learning; Artificial intelligence; Markov Decision Processes; Random forests

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

APA (6th Edition):

Hester, T. (2012). Texplore : temporal difference reinforcement learning for robots and time-constrained domains: Temporal difference reinforcement learning for robots and time-constrained domains. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/ETD-UT-2012-12-6763

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):

Hester, Todd. “Texplore : temporal difference reinforcement learning for robots and time-constrained domains: Temporal difference reinforcement learning for robots and time-constrained domains.” 2012. Thesis, University of Texas – Austin. Accessed June 25, 2019. http://hdl.handle.net/2152/ETD-UT-2012-12-6763.

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

MLA Handbook (7th Edition):

Hester, Todd. “Texplore : temporal difference reinforcement learning for robots and time-constrained domains: Temporal difference reinforcement learning for robots and time-constrained domains.” 2012. Web. 25 Jun 2019.

Vancouver:

Hester T. Texplore : temporal difference reinforcement learning for robots and time-constrained domains: Temporal difference reinforcement learning for robots and time-constrained domains. [Internet] [Thesis]. University of Texas – Austin; 2012. [cited 2019 Jun 25]. Available from: http://hdl.handle.net/2152/ETD-UT-2012-12-6763.

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

Council of Science Editors:

Hester T. Texplore : temporal difference reinforcement learning for robots and time-constrained domains: Temporal difference reinforcement learning for robots and time-constrained domains. [Thesis]. University of Texas – Austin; 2012. Available from: http://hdl.handle.net/2152/ETD-UT-2012-12-6763

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

17. Moon, Taesun, Ph. D. Word meaning in context as a paraphrase distribution : evidence, learning, and inference.

Degree: Linguistics, 2011, University of Texas – Austin

 In this dissertation, we introduce a graph-based model of instance-based, usage meaning that is cast as a problem of probabilistic inference. The main aim of… (more)

Subjects/Keywords: Computational linguistics; Lexical semantics; Probabilistic graphical models; Natural language processing; Word sense disambiguation; Paraphrasing

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

APA (6th Edition):

Moon, Taesun, P. D. (2011). Word meaning in context as a paraphrase distribution : evidence, learning, and inference. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/ETD-UT-2011-08-4143

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):

Moon, Taesun, Ph D. “Word meaning in context as a paraphrase distribution : evidence, learning, and inference.” 2011. Thesis, University of Texas – Austin. Accessed June 25, 2019. http://hdl.handle.net/2152/ETD-UT-2011-08-4143.

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

MLA Handbook (7th Edition):

Moon, Taesun, Ph D. “Word meaning in context as a paraphrase distribution : evidence, learning, and inference.” 2011. Web. 25 Jun 2019.

Vancouver:

Moon, Taesun PD. Word meaning in context as a paraphrase distribution : evidence, learning, and inference. [Internet] [Thesis]. University of Texas – Austin; 2011. [cited 2019 Jun 25]. Available from: http://hdl.handle.net/2152/ETD-UT-2011-08-4143.

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

Council of Science Editors:

Moon, Taesun PD. Word meaning in context as a paraphrase distribution : evidence, learning, and inference. [Thesis]. University of Texas – Austin; 2011. Available from: http://hdl.handle.net/2152/ETD-UT-2011-08-4143

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

18. Xu, Changhai, 1977-. Steps towards the object semantic hierarchy.

Degree: Computer Sciences, 2011, University of Texas – Austin

 An intelligent robot must be able to perceive and reason robustly about its world in terms of objects, among other foundational concepts. The robot can… (more)

Subjects/Keywords: Object semantic hierarchy; 3D object model; Object tracking; Object segmentation; Motion segmentation; 3D pose estimation

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APA (6th Edition):

Xu, Changhai, 1. (2011). Steps towards the object semantic hierarchy. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/ETD-UT-2011-08-3797

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

Chicago Manual of Style (16th Edition):

Xu, Changhai, 1977-. “Steps towards the object semantic hierarchy.” 2011. Thesis, University of Texas – Austin. Accessed June 25, 2019. http://hdl.handle.net/2152/ETD-UT-2011-08-3797.

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

MLA Handbook (7th Edition):

Xu, Changhai, 1977-. “Steps towards the object semantic hierarchy.” 2011. Web. 25 Jun 2019.

Vancouver:

Xu, Changhai 1. Steps towards the object semantic hierarchy. [Internet] [Thesis]. University of Texas – Austin; 2011. [cited 2019 Jun 25]. Available from: http://hdl.handle.net/2152/ETD-UT-2011-08-3797.

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

Council of Science Editors:

Xu, Changhai 1. Steps towards the object semantic hierarchy. [Thesis]. University of Texas – Austin; 2011. Available from: http://hdl.handle.net/2152/ETD-UT-2011-08-3797

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

19. Lockett, Alan Justin. General-purpose optimization through information maximization.

Degree: Computer Sciences, 2012, University of Texas – Austin

 The primary goal of artificial intelligence research is to develop a machine capable of learning to solve disparate real-world tasks autonomously, without relying on specialized… (more)

Subjects/Keywords: Optimization; General-purpose learning; Martingale optimization; Artificial intelligence; Evolutionary computation; Genetic algorithms; Simulated annealing; Evolutionary annealing; Neuroannealing; Neural networks; Neural network controllers; Neuroevolution; Differential evolution; No Free Lunch theorems; NFL Identification Theorem; Population-based stochastic optimization; Iterative optimization; Optimal optimization; Information-maximization principle; Convex control; Algorithm selection

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

APA (6th Edition):

Lockett, A. J. (2012). General-purpose optimization through information maximization. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/ETD-UT-2012-05-5459

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):

Lockett, Alan Justin. “General-purpose optimization through information maximization.” 2012. Thesis, University of Texas – Austin. Accessed June 25, 2019. http://hdl.handle.net/2152/ETD-UT-2012-05-5459.

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

MLA Handbook (7th Edition):

Lockett, Alan Justin. “General-purpose optimization through information maximization.” 2012. Web. 25 Jun 2019.

Vancouver:

Lockett AJ. General-purpose optimization through information maximization. [Internet] [Thesis]. University of Texas – Austin; 2012. [cited 2019 Jun 25]. Available from: http://hdl.handle.net/2152/ETD-UT-2012-05-5459.

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

Council of Science Editors:

Lockett AJ. General-purpose optimization through information maximization. [Thesis]. University of Texas – Austin; 2012. Available from: http://hdl.handle.net/2152/ETD-UT-2012-05-5459

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

20. Vijaya Raghavan, Sindhu. Bayesian Logic Programs for plan recognition and machine reading.

Degree: Computer Sciences, 2012, University of Texas – Austin

 Several real world tasks involve data that is uncertain and relational in nature. Traditional approaches like first-order logic and probabilistic models either deal with structured… (more)

Subjects/Keywords: Bayesian Logic Programs; Statistical relational learning; Plan recognition; Abductive reasoning; Machine reading; Rule learning; Information extraction; BLPs; SRL; IE; BALPs; Bayesian Abductive Logic Programs

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APA (6th Edition):

Vijaya Raghavan, S. (2012). Bayesian Logic Programs for plan recognition and machine reading. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/19544

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):

Vijaya Raghavan, Sindhu. “Bayesian Logic Programs for plan recognition and machine reading.” 2012. Thesis, University of Texas – Austin. Accessed June 25, 2019. http://hdl.handle.net/2152/19544.

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

MLA Handbook (7th Edition):

Vijaya Raghavan, Sindhu. “Bayesian Logic Programs for plan recognition and machine reading.” 2012. Web. 25 Jun 2019.

Vancouver:

Vijaya Raghavan S. Bayesian Logic Programs for plan recognition and machine reading. [Internet] [Thesis]. University of Texas – Austin; 2012. [cited 2019 Jun 25]. Available from: http://hdl.handle.net/2152/19544.

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

Council of Science Editors:

Vijaya Raghavan S. Bayesian Logic Programs for plan recognition and machine reading. [Thesis]. University of Texas – Austin; 2012. Available from: http://hdl.handle.net/2152/19544

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

21. Knox, William Bradley. Learning from human-generated reward.

Degree: Computer Sciences, 2012, University of Texas – Austin

 Robots and other computational agents are increasingly becoming part of our daily lives. They will need to be able to learn to perform new tasks,… (more)

Subjects/Keywords: Human-agent interaction; Human-robot interaction; Human teachers; Shaping; Learning agents; Reinforcement learning; Robotics; Machine learning

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

APA (6th Edition):

Knox, W. B. (2012). Learning from human-generated reward. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/19472

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):

Knox, William Bradley. “Learning from human-generated reward.” 2012. Thesis, University of Texas – Austin. Accessed June 25, 2019. http://hdl.handle.net/2152/19472.

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

MLA Handbook (7th Edition):

Knox, William Bradley. “Learning from human-generated reward.” 2012. Web. 25 Jun 2019.

Vancouver:

Knox WB. Learning from human-generated reward. [Internet] [Thesis]. University of Texas – Austin; 2012. [cited 2019 Jun 25]. Available from: http://hdl.handle.net/2152/19472.

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

Council of Science Editors:

Knox WB. Learning from human-generated reward. [Thesis]. University of Texas – Austin; 2012. Available from: http://hdl.handle.net/2152/19472

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

22. Ponvert, Elias Franchot. Unsupervised partial parsing.

Degree: Linguistics, 2011, University of Texas – Austin

 The subject matter of this thesis is the problem of learning to discover grammatical structure from raw text alone, without access to explicit instruction or… (more)

Subjects/Keywords: Computational linguistics; Natural language processing; Unsupervised; Parsing; Chunking; Text processing

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APA (6th Edition):

Ponvert, E. F. (2011). Unsupervised partial parsing. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/ETD-UT-2011-08-3991

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):

Ponvert, Elias Franchot. “Unsupervised partial parsing.” 2011. Thesis, University of Texas – Austin. Accessed June 25, 2019. http://hdl.handle.net/2152/ETD-UT-2011-08-3991.

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

MLA Handbook (7th Edition):

Ponvert, Elias Franchot. “Unsupervised partial parsing.” 2011. Web. 25 Jun 2019.

Vancouver:

Ponvert EF. Unsupervised partial parsing. [Internet] [Thesis]. University of Texas – Austin; 2011. [cited 2019 Jun 25]. Available from: http://hdl.handle.net/2152/ETD-UT-2011-08-3991.

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

Council of Science Editors:

Ponvert EF. Unsupervised partial parsing. [Thesis]. University of Texas – Austin; 2011. Available from: http://hdl.handle.net/2152/ETD-UT-2011-08-3991

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

23. Kim, Doo Soon. Knowledge integration in machine reading.

Degree: Computer Sciences, 2011, University of Texas – Austin

 Machine reading is the artificial-intelligence task of automatically reading a corpus of texts and, from the contents, building a knowledge base that supports automated reasoning… (more)

Subjects/Keywords: Machine reading; Knowledge integration; Artificial intelligence; Text understanding; NLP

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APA (6th Edition):

Kim, D. S. (2011). Knowledge integration in machine reading. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/ETD-UT-2011-08-4049

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):

Kim, Doo Soon. “Knowledge integration in machine reading.” 2011. Thesis, University of Texas – Austin. Accessed June 25, 2019. http://hdl.handle.net/2152/ETD-UT-2011-08-4049.

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

MLA Handbook (7th Edition):

Kim, Doo Soon. “Knowledge integration in machine reading.” 2011. Web. 25 Jun 2019.

Vancouver:

Kim DS. Knowledge integration in machine reading. [Internet] [Thesis]. University of Texas – Austin; 2011. [cited 2019 Jun 25]. Available from: http://hdl.handle.net/2152/ETD-UT-2011-08-4049.

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

Council of Science Editors:

Kim DS. Knowledge integration in machine reading. [Thesis]. University of Texas – Austin; 2011. Available from: http://hdl.handle.net/2152/ETD-UT-2011-08-4049

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

24. Pardoe, David Merrill. Adaptive trading agent strategies using market experience.

Degree: Computer Sciences, 2011, University of Texas – Austin

 Along with the growth of electronic commerce has come an interest in developing autonomous trading agents. Often, such agents must interact directly with other market… (more)

Subjects/Keywords: Trading agents; Multi-agent systems; Machine learning; Transfer learning; Supply chain management; Ad auctions; Trading Agent Competition

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APA (6th Edition):

Pardoe, D. M. (2011). Adaptive trading agent strategies using market experience. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/ETD-UT-2011-05-2899

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):

Pardoe, David Merrill. “Adaptive trading agent strategies using market experience.” 2011. Thesis, University of Texas – Austin. Accessed June 25, 2019. http://hdl.handle.net/2152/ETD-UT-2011-05-2899.

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

MLA Handbook (7th Edition):

Pardoe, David Merrill. “Adaptive trading agent strategies using market experience.” 2011. Web. 25 Jun 2019.

Vancouver:

Pardoe DM. Adaptive trading agent strategies using market experience. [Internet] [Thesis]. University of Texas – Austin; 2011. [cited 2019 Jun 25]. Available from: http://hdl.handle.net/2152/ETD-UT-2011-05-2899.

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

Council of Science Editors:

Pardoe DM. Adaptive trading agent strategies using market experience. [Thesis]. University of Texas – Austin; 2011. Available from: http://hdl.handle.net/2152/ETD-UT-2011-05-2899

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

25. Chen, David Lieh-Chiang. Learning language from ambiguous perceptual context.

Degree: Computer Sciences, 2012, University of Texas – Austin

 Building a computer system that can understand human languages has been one of the long-standing goals of artificial intelligence. Currently, most state-of-the-art natural language processing… (more)

Subjects/Keywords: Natural language processing; Natural language learning; Connecting language and perception; Machine learning; Artificial intelligence

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

APA (6th Edition):

Chen, D. L. (2012). Learning language from ambiguous perceptual context. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/ETD-UT-2012-05-5203

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):

Chen, David Lieh-Chiang. “Learning language from ambiguous perceptual context.” 2012. Thesis, University of Texas – Austin. Accessed June 25, 2019. http://hdl.handle.net/2152/ETD-UT-2012-05-5203.

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

MLA Handbook (7th Edition):

Chen, David Lieh-Chiang. “Learning language from ambiguous perceptual context.” 2012. Web. 25 Jun 2019.

Vancouver:

Chen DL. Learning language from ambiguous perceptual context. [Internet] [Thesis]. University of Texas – Austin; 2012. [cited 2019 Jun 25]. Available from: http://hdl.handle.net/2152/ETD-UT-2012-05-5203.

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

Council of Science Editors:

Chen DL. Learning language from ambiguous perceptual context. [Thesis]. University of Texas – Austin; 2012. Available from: http://hdl.handle.net/2152/ETD-UT-2012-05-5203

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

.