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You searched for +publisher:"University of Texas – Austin" +contributor:("Mooney, Raymond J."). Showing records 1 – 30 of 37 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 April 19, 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. 19 Apr 2019.

Vancouver:

Reisinger JS. Latent variable models of distributional lexical semantics. [Internet] [Thesis]. University of Texas – Austin; 2012. [cited 2019 Apr 19]. 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. Mihalkova, Lilyana Simeonova. Learning with Markov logic networks : transfer learning, structure learning, and an application to Web query disambiguation.

Degree: Computer Sciences, 2009, University of Texas – Austin

 Traditionally, machine learning algorithms assume that training data is provided as a set of independent instances, each of which can be described as a feature… (more)

Subjects/Keywords: Markov logic networks; Web query disambiguation; Statistical Relational Learning; Artificial intelligence; Machine learning; Multi-relational data; First-order logic; Probability distribution; Structure learning; Transfer learning; Algorithms

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APA (6th Edition):

Mihalkova, L. S. (2009). Learning with Markov logic networks : transfer learning, structure learning, and an application to Web query disambiguation. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/10574

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):

Mihalkova, Lilyana Simeonova. “Learning with Markov logic networks : transfer learning, structure learning, and an application to Web query disambiguation.” 2009. Thesis, University of Texas – Austin. Accessed April 19, 2019. http://hdl.handle.net/2152/10574.

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

MLA Handbook (7th Edition):

Mihalkova, Lilyana Simeonova. “Learning with Markov logic networks : transfer learning, structure learning, and an application to Web query disambiguation.” 2009. Web. 19 Apr 2019.

Vancouver:

Mihalkova LS. Learning with Markov logic networks : transfer learning, structure learning, and an application to Web query disambiguation. [Internet] [Thesis]. University of Texas – Austin; 2009. [cited 2019 Apr 19]. Available from: http://hdl.handle.net/2152/10574.

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

Council of Science Editors:

Mihalkova LS. Learning with Markov logic networks : transfer learning, structure learning, and an application to Web query disambiguation. [Thesis]. University of Texas – Austin; 2009. Available from: http://hdl.handle.net/2152/10574

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


University of Texas – Austin

3. 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 April 19, 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. 19 Apr 2019.

Vancouver:

Ge R. Learning for semantic parsing using statistical syntactic parsing techniques. [Internet] [Thesis]. University of Texas – Austin; 2010. [cited 2019 Apr 19]. 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

4. Ramanujam, Srivatsan. Factorial Hidden Markov Models for full and weakly supervised supertagging.

Degree: Computer Sciences, 2009, University of Texas – Austin

 For many sequence prediction tasks in Natural Language Processing, modeling dependencies between individual predictions can be used to improve prediction accuracy of the sequence as… (more)

Subjects/Keywords: Hidden Markov Models; Bayesian Models; Categorial Grammar; Supertagging; Joint Inference

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APA (6th Edition):

Ramanujam, S. (2009). Factorial Hidden Markov Models for full and weakly supervised supertagging. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/ETD-UT-2009-08-350

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):

Ramanujam, Srivatsan. “Factorial Hidden Markov Models for full and weakly supervised supertagging.” 2009. Thesis, University of Texas – Austin. Accessed April 19, 2019. http://hdl.handle.net/2152/ETD-UT-2009-08-350.

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

MLA Handbook (7th Edition):

Ramanujam, Srivatsan. “Factorial Hidden Markov Models for full and weakly supervised supertagging.” 2009. Web. 19 Apr 2019.

Vancouver:

Ramanujam S. Factorial Hidden Markov Models for full and weakly supervised supertagging. [Internet] [Thesis]. University of Texas – Austin; 2009. [cited 2019 Apr 19]. Available from: http://hdl.handle.net/2152/ETD-UT-2009-08-350.

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

Council of Science Editors:

Ramanujam S. Factorial Hidden Markov Models for full and weakly supervised supertagging. [Thesis]. University of Texas – Austin; 2009. Available from: http://hdl.handle.net/2152/ETD-UT-2009-08-350

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


University of Texas – Austin

5. Gupta, Sonal. Activity retrieval in closed captioned videos.

Degree: Computer Sciences, 2009, University of Texas – Austin

 Recognizing activities in real-world videos is a difficult problem exacerbated by background clutter, changes in camera angle & zoom, occlusion and rapid camera movements. Large… (more)

Subjects/Keywords: Activity Recognition; Action Recognition; Video Retrieval; Machine Learning; Computer Vision; Multimedia; Closed Captions

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

APA (6th Edition):

Gupta, S. (2009). Activity retrieval in closed captioned videos. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/ETD-UT-2009-08-305

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):

Gupta, Sonal. “Activity retrieval in closed captioned videos.” 2009. Thesis, University of Texas – Austin. Accessed April 19, 2019. http://hdl.handle.net/2152/ETD-UT-2009-08-305.

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

MLA Handbook (7th Edition):

Gupta, Sonal. “Activity retrieval in closed captioned videos.” 2009. Web. 19 Apr 2019.

Vancouver:

Gupta S. Activity retrieval in closed captioned videos. [Internet] [Thesis]. University of Texas – Austin; 2009. [cited 2019 Apr 19]. Available from: http://hdl.handle.net/2152/ETD-UT-2009-08-305.

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

Council of Science Editors:

Gupta S. Activity retrieval in closed captioned videos. [Thesis]. University of Texas – Austin; 2009. Available from: http://hdl.handle.net/2152/ETD-UT-2009-08-305

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


University of Texas – Austin

6. Chaurasia, Shobhit. Dialog for natural language to code.

Degree: Computer Sciences, 2017, University of Texas – Austin

 Generating computer code from natural language descriptions has been a long-standing problem in computational linguistics. Prior work in this domain has restricted itself to generating… (more)

Subjects/Keywords: Dialog; Interactive systems; Neural network; Deep learning; Semantic parsing; Computer code; Software engineering

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

APA (6th Edition):

Chaurasia, S. (2017). Dialog for natural language to code. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/62886

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):

Chaurasia, Shobhit. “Dialog for natural language to code.” 2017. Thesis, University of Texas – Austin. Accessed April 19, 2019. http://hdl.handle.net/2152/62886.

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

MLA Handbook (7th Edition):

Chaurasia, Shobhit. “Dialog for natural language to code.” 2017. Web. 19 Apr 2019.

Vancouver:

Chaurasia S. Dialog for natural language to code. [Internet] [Thesis]. University of Texas – Austin; 2017. [cited 2019 Apr 19]. Available from: http://hdl.handle.net/2152/62886.

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

Council of Science Editors:

Chaurasia S. Dialog for natural language to code. [Thesis]. University of Texas – Austin; 2017. Available from: http://hdl.handle.net/2152/62886

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


University of Texas – Austin

7. Roller, Stephen Creig. Identifying lexical relationships and entailments with distributional semantics.

Degree: Computer Sciences, 2017, University of Texas – Austin

 Many modern efforts in Natural Language Understanding depend on rich and powerful semantic representations of words. Systems for sophisticated logical and textual reasoning often depend… (more)

Subjects/Keywords: Natural language processing; Lexical semantics; Lexical relationships; Hypernymy; Distributional semantics

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APA (6th Edition):

Roller, S. C. (2017). Identifying lexical relationships and entailments with distributional semantics. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/61528

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):

Roller, Stephen Creig. “Identifying lexical relationships and entailments with distributional semantics.” 2017. Thesis, University of Texas – Austin. Accessed April 19, 2019. http://hdl.handle.net/2152/61528.

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

MLA Handbook (7th Edition):

Roller, Stephen Creig. “Identifying lexical relationships and entailments with distributional semantics.” 2017. Web. 19 Apr 2019.

Vancouver:

Roller SC. Identifying lexical relationships and entailments with distributional semantics. [Internet] [Thesis]. University of Texas – Austin; 2017. [cited 2019 Apr 19]. Available from: http://hdl.handle.net/2152/61528.

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

Council of Science Editors:

Roller SC. Identifying lexical relationships and entailments with distributional semantics. [Thesis]. University of Texas – Austin; 2017. Available from: http://hdl.handle.net/2152/61528

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


University of Texas – Austin

8. -9199-0633. Continually improving grounded natural language understanding through human-robot dialog.

Degree: Computer Sciences, 2018, University of Texas – Austin

 As robots become ubiquitous in homes and workplaces such as hospitals and factories, they must be able to communicate with humans. Several kinds of knowledge… (more)

Subjects/Keywords: Natural language processing; Human-robot dialog

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

APA (6th Edition):

-9199-0633. (2018). Continually improving grounded natural language understanding through human-robot dialog. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/68120

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

Chicago Manual of Style (16th Edition):

-9199-0633. “Continually improving grounded natural language understanding through human-robot dialog.” 2018. Thesis, University of Texas – Austin. Accessed April 19, 2019. http://hdl.handle.net/2152/68120.

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

MLA Handbook (7th Edition):

-9199-0633. “Continually improving grounded natural language understanding through human-robot dialog.” 2018. Web. 19 Apr 2019.

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

Vancouver:

-9199-0633. Continually improving grounded natural language understanding through human-robot dialog. [Internet] [Thesis]. University of Texas – Austin; 2018. [cited 2019 Apr 19]. Available from: http://hdl.handle.net/2152/68120.

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

Council of Science Editors:

-9199-0633. Continually improving grounded natural language understanding through human-robot dialog. [Thesis]. University of Texas – Austin; 2018. Available from: http://hdl.handle.net/2152/68120

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


University of Texas – Austin

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

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 April 19, 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. 19 Apr 2019.

Vancouver:

Kuhlmann GJ. Automated domain analysis and transfer learning in general game playing. [Internet] [Thesis]. University of Texas – Austin; 2010. [cited 2019 Apr 19]. 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

10. Whang, Joyce Jiyoung. Overlapping community detection in massive social networks.

Degree: Computer Sciences, 2015, University of Texas – Austin

 Massive social networks have become increasingly popular in recent years. Community detection is one of the most important techniques for the analysis of such complex… (more)

Subjects/Keywords: Community detection; Clustering; Social networks; Overlapping communities; Overlapping clusters; Non-exhaustive clustering; Seed expansion; K-means; Semidefinite programming; Co-clustering; PageRank; Data-driven algorithm; Scalable computing

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APA (6th Edition):

Whang, J. J. (2015). Overlapping community detection in massive social networks. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/33272

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):

Whang, Joyce Jiyoung. “Overlapping community detection in massive social networks.” 2015. Thesis, University of Texas – Austin. Accessed April 19, 2019. http://hdl.handle.net/2152/33272.

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

MLA Handbook (7th Edition):

Whang, Joyce Jiyoung. “Overlapping community detection in massive social networks.” 2015. Web. 19 Apr 2019.

Vancouver:

Whang JJ. Overlapping community detection in massive social networks. [Internet] [Thesis]. University of Texas – Austin; 2015. [cited 2019 Apr 19]. Available from: http://hdl.handle.net/2152/33272.

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

Council of Science Editors:

Whang JJ. Overlapping community detection in massive social networks. [Thesis]. University of Texas – Austin; 2015. Available from: http://hdl.handle.net/2152/33272

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


University of Texas – Austin

11. Chen, Chao-Yeh. Learning human activities and poses with interconnected data sources.

Degree: Computer Sciences, 2016, University of Texas – Austin

 Understanding human actions and poses in images or videos is a challenging problem in computer vision. There are different topics related to this problem such… (more)

Subjects/Keywords: Activity recognition; Activity detection

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

APA (6th Edition):

Chen, C. (2016). Learning human activities and poses with interconnected data sources. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/40260

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, Chao-Yeh. “Learning human activities and poses with interconnected data sources.” 2016. Thesis, University of Texas – Austin. Accessed April 19, 2019. http://hdl.handle.net/2152/40260.

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

MLA Handbook (7th Edition):

Chen, Chao-Yeh. “Learning human activities and poses with interconnected data sources.” 2016. Web. 19 Apr 2019.

Vancouver:

Chen C. Learning human activities and poses with interconnected data sources. [Internet] [Thesis]. University of Texas – Austin; 2016. [cited 2019 Apr 19]. Available from: http://hdl.handle.net/2152/40260.

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

Council of Science Editors:

Chen C. Learning human activities and poses with interconnected data sources. [Thesis]. University of Texas – Austin; 2016. Available from: http://hdl.handle.net/2152/40260

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


University of Texas – Austin

12. 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 April 19, 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. 19 Apr 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 Apr 19]. 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

13. Garrette, Daniel Hunter. Inducing grammars from linguistic universals and realistic amounts of supervision.

Degree: Computer Sciences, 2015, University of Texas – Austin

 The best performing NLP models to date are learned from large volumes of manually-annotated data. For tasks like part-of-speech tagging and grammatical parsing, high performance… (more)

Subjects/Keywords: Computer science; Artificial intelligence; Natural language processing; Machine learning; Bayesian statistics; Grammar induction; Parsing; Computational linguistics

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APA (6th Edition):

Garrette, D. H. (2015). Inducing grammars from linguistic universals and realistic amounts of supervision. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/44478

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):

Garrette, Daniel Hunter. “Inducing grammars from linguistic universals and realistic amounts of supervision.” 2015. Thesis, University of Texas – Austin. Accessed April 19, 2019. http://hdl.handle.net/2152/44478.

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

MLA Handbook (7th Edition):

Garrette, Daniel Hunter. “Inducing grammars from linguistic universals and realistic amounts of supervision.” 2015. Web. 19 Apr 2019.

Vancouver:

Garrette DH. Inducing grammars from linguistic universals and realistic amounts of supervision. [Internet] [Thesis]. University of Texas – Austin; 2015. [cited 2019 Apr 19]. Available from: http://hdl.handle.net/2152/44478.

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

Council of Science Editors:

Garrette DH. Inducing grammars from linguistic universals and realistic amounts of supervision. [Thesis]. University of Texas – Austin; 2015. Available from: http://hdl.handle.net/2152/44478

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


University of Texas – Austin

14. -4493-3358. Appropriate, accessible and appealing probabilistic graphical models.

Degree: Computer Sciences, 2017, University of Texas – Austin

 Appropriate - Many multivariate probabilistic models either use independent distributions or dependent Gaussian distributions. Yet, many real-world datasets contain count-valued or non-negative skewed data, e.g.… (more)

Subjects/Keywords: Graphical models; Topic models; Poisson; Count data; Visualization; Human computer interaction

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APA (6th Edition):

-4493-3358. (2017). Appropriate, accessible and appealing probabilistic graphical models. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/62986

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

Chicago Manual of Style (16th Edition):

-4493-3358. “Appropriate, accessible and appealing probabilistic graphical models.” 2017. Thesis, University of Texas – Austin. Accessed April 19, 2019. http://hdl.handle.net/2152/62986.

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

MLA Handbook (7th Edition):

-4493-3358. “Appropriate, accessible and appealing probabilistic graphical models.” 2017. Web. 19 Apr 2019.

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

Vancouver:

-4493-3358. Appropriate, accessible and appealing probabilistic graphical models. [Internet] [Thesis]. University of Texas – Austin; 2017. [cited 2019 Apr 19]. Available from: http://hdl.handle.net/2152/62986.

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

Council of Science Editors:

-4493-3358. Appropriate, accessible and appealing probabilistic graphical models. [Thesis]. University of Texas – Austin; 2017. Available from: http://hdl.handle.net/2152/62986

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


University of Texas – Austin

15. -3729-8456. Natural-language video description with deep recurrent neural networks.

Degree: Computer Sciences, 2017, University of Texas – Austin

 For most people, watching a brief video and describing what happened (in words) is an easy task. For machines, extracting meaning from video pixels and… (more)

Subjects/Keywords: Video; Captioning; Description; LSTM; RNN; Recurrent; Neural networks; Image captioning; Video captioning; Language and vision

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APA (6th Edition):

-3729-8456. (2017). Natural-language video description with deep recurrent neural networks. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/62987

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

Chicago Manual of Style (16th Edition):

-3729-8456. “Natural-language video description with deep recurrent neural networks.” 2017. Thesis, University of Texas – Austin. Accessed April 19, 2019. http://hdl.handle.net/2152/62987.

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

MLA Handbook (7th Edition):

-3729-8456. “Natural-language video description with deep recurrent neural networks.” 2017. Web. 19 Apr 2019.

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

Vancouver:

-3729-8456. Natural-language video description with deep recurrent neural networks. [Internet] [Thesis]. University of Texas – Austin; 2017. [cited 2019 Apr 19]. Available from: http://hdl.handle.net/2152/62987.

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

Council of Science Editors:

-3729-8456. Natural-language video description with deep recurrent neural networks. [Thesis]. University of Texas – Austin; 2017. Available from: http://hdl.handle.net/2152/62987

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


University of Texas – Austin

16. -7062-2970. Advances in statistical script learning.

Degree: Computer Sciences, 2017, University of Texas – Austin

 When humans encode information into natural language, they do so with the clear assumption that the reader will be able to seamlessly make inferences based… (more)

Subjects/Keywords: Natural language processing; Machine learning

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APA (6th Edition):

-7062-2970. (2017). Advances in statistical script learning. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/63480

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

Chicago Manual of Style (16th Edition):

-7062-2970. “Advances in statistical script learning.” 2017. Thesis, University of Texas – Austin. Accessed April 19, 2019. http://hdl.handle.net/2152/63480.

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

MLA Handbook (7th Edition):

-7062-2970. “Advances in statistical script learning.” 2017. Web. 19 Apr 2019.

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

Vancouver:

-7062-2970. Advances in statistical script learning. [Internet] [Thesis]. University of Texas – Austin; 2017. [cited 2019 Apr 19]. Available from: http://hdl.handle.net/2152/63480.

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

Council of Science Editors:

-7062-2970. Advances in statistical script learning. [Thesis]. University of Texas – Austin; 2017. Available from: http://hdl.handle.net/2152/63480

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

17. -3023-4337. Knowledge transfer using latent variable models.

Degree: Electrical and Computer Engineering, 2015, University of Texas – Austin

 In several applications, scarcity of labeled data is a challenging problem that hinders the predictive capabilities of machine learning algorithms. Additionally, the distribution of the… (more)

Subjects/Keywords: Transfer learning; Multitask learning; Gamma process; Poisson factorization; Supervised topic model

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APA (6th Edition):

-3023-4337. (2015). Knowledge transfer using latent variable models. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/31414

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

Chicago Manual of Style (16th Edition):

-3023-4337. “Knowledge transfer using latent variable models.” 2015. Thesis, University of Texas – Austin. Accessed April 19, 2019. http://hdl.handle.net/2152/31414.

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

MLA Handbook (7th Edition):

-3023-4337. “Knowledge transfer using latent variable models.” 2015. Web. 19 Apr 2019.

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

Vancouver:

-3023-4337. Knowledge transfer using latent variable models. [Internet] [Thesis]. University of Texas – Austin; 2015. [cited 2019 Apr 19]. Available from: http://hdl.handle.net/2152/31414.

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

Council of Science Editors:

-3023-4337. Knowledge transfer using latent variable models. [Thesis]. University of Texas – Austin; 2015. Available from: http://hdl.handle.net/2152/31414

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

18. Beltagy, Islam Kamel Ahmed. Natural language semantics using probabilistic logic.

Degree: Computer Sciences, 2016, University of Texas – Austin

 With better natural language semantic representations, computers can do more applications more efficiently as a result of better understanding of natural text. However, no single… (more)

Subjects/Keywords: NLP; Machine learning; Probabilistic logic; MLNs; PSL; Markov Logic Network; QA; RTE; STS

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APA (6th Edition):

Beltagy, I. K. A. (2016). Natural language semantics using probabilistic logic. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/46617

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):

Beltagy, Islam Kamel Ahmed. “Natural language semantics using probabilistic logic.” 2016. Thesis, University of Texas – Austin. Accessed April 19, 2019. http://hdl.handle.net/2152/46617.

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

MLA Handbook (7th Edition):

Beltagy, Islam Kamel Ahmed. “Natural language semantics using probabilistic logic.” 2016. Web. 19 Apr 2019.

Vancouver:

Beltagy IKA. Natural language semantics using probabilistic logic. [Internet] [Thesis]. University of Texas – Austin; 2016. [cited 2019 Apr 19]. Available from: http://hdl.handle.net/2152/46617.

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

Council of Science Editors:

Beltagy IKA. Natural language semantics using probabilistic logic. [Thesis]. University of Texas – Austin; 2016. Available from: http://hdl.handle.net/2152/46617

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


University of Texas – Austin

19. 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 April 19, 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. 19 Apr 2019.

Vancouver:

Ramakrishnan SR. A systems approach to computational protein identification. [Internet] [Thesis]. University of Texas – Austin; 2010. [cited 2019 Apr 19]. 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

20. 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 April 19, 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. 19 Apr 2019.

Vancouver:

Vijayanarasimhan S. Active visual category learning. [Internet] [Thesis]. University of Texas – Austin; 2011. [cited 2019 Apr 19]. 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

21. 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 April 19, 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. 19 Apr 2019.

Vancouver:

Grasemann HU. A computational model of language pathology in schizophrenia. [Internet] [Thesis]. University of Texas – Austin; 2010. [cited 2019 Apr 19]. 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

22. 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 April 19, 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. 19 Apr 2019.

Vancouver:

Kim, Joo Hyun a2. Grounded language learning models for ambiguous supervision. [Internet] [Thesis]. University of Texas – Austin; 2013. [cited 2019 Apr 19]. 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

23. Viswanathan, Vidhoon. Knowledge base population using stacked ensembles of information extractors.

Degree: Computer Sciences, 2015, University of Texas – Austin

 The performance of relation extractors plays a significant role in automatic creation of knowledge bases from web corpus. Using automated systems to create knowledge bases… (more)

Subjects/Keywords: KBP; Slot filling; Information extraction

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APA (6th Edition):

Viswanathan, V. (2015). Knowledge base population using stacked ensembles of information extractors. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/31849

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):

Viswanathan, Vidhoon. “Knowledge base population using stacked ensembles of information extractors.” 2015. Thesis, University of Texas – Austin. Accessed April 19, 2019. http://hdl.handle.net/2152/31849.

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

MLA Handbook (7th Edition):

Viswanathan, Vidhoon. “Knowledge base population using stacked ensembles of information extractors.” 2015. Web. 19 Apr 2019.

Vancouver:

Viswanathan V. Knowledge base population using stacked ensembles of information extractors. [Internet] [Thesis]. University of Texas – Austin; 2015. [cited 2019 Apr 19]. Available from: http://hdl.handle.net/2152/31849.

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

Council of Science Editors:

Viswanathan V. Knowledge base population using stacked ensembles of information extractors. [Thesis]. University of Texas – Austin; 2015. Available from: http://hdl.handle.net/2152/31849

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

24. 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 April 19, 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. 19 Apr 2019.

Vancouver:

Acharya A. Combining classifier and cluster ensembles for semi-supervised and transfer learning. [Internet] [Thesis]. University of Texas – Austin; 2012. [cited 2019 Apr 19]. 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


University of Texas – Austin

25. Bilenko, Mikhail Yuryevich. Learnable similarity functions and their application to record linkage and clustering.

Degree: Computer Sciences, 2006, University of Texas – Austin

 Many machine learning and data mining tasks depend on functions that estimate similarity between instances. Similarity computations are particularly important in clustering and information integration… (more)

Subjects/Keywords: Cluster analysis – Data processing; Pattern recognition systems; Machine learning

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APA (6th Edition):

Bilenko, M. Y. (2006). Learnable similarity functions and their application to record linkage and clustering. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/2681

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):

Bilenko, Mikhail Yuryevich. “Learnable similarity functions and their application to record linkage and clustering.” 2006. Thesis, University of Texas – Austin. Accessed April 19, 2019. http://hdl.handle.net/2152/2681.

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

MLA Handbook (7th Edition):

Bilenko, Mikhail Yuryevich. “Learnable similarity functions and their application to record linkage and clustering.” 2006. Web. 19 Apr 2019.

Vancouver:

Bilenko MY. Learnable similarity functions and their application to record linkage and clustering. [Internet] [Thesis]. University of Texas – Austin; 2006. [cited 2019 Apr 19]. Available from: http://hdl.handle.net/2152/2681.

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

Council of Science Editors:

Bilenko MY. Learnable similarity functions and their application to record linkage and clustering. [Thesis]. University of Texas – Austin; 2006. Available from: http://hdl.handle.net/2152/2681

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


University of Texas – Austin

26. Bunescu, Razvan Constantin, 1975-. Learning for information extraction: from named entity recognition and disambiguation to relation extraction.

Degree: Computer Sciences, 2007, University of Texas – Austin

 Information Extraction, the task of locating textual mentions of specific types of entities and their relationships, aims at representing the information contained in text documents… (more)

Subjects/Keywords: Natural language processing (Computer science); Information storage and retrieval systems

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APA (6th Edition):

Bunescu, Razvan Constantin, 1. (2007). Learning for information extraction: from named entity recognition and disambiguation to relation extraction. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/3200

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):

Bunescu, Razvan Constantin, 1975-. “Learning for information extraction: from named entity recognition and disambiguation to relation extraction.” 2007. Thesis, University of Texas – Austin. Accessed April 19, 2019. http://hdl.handle.net/2152/3200.

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

MLA Handbook (7th Edition):

Bunescu, Razvan Constantin, 1975-. “Learning for information extraction: from named entity recognition and disambiguation to relation extraction.” 2007. Web. 19 Apr 2019.

Vancouver:

Bunescu, Razvan Constantin 1. Learning for information extraction: from named entity recognition and disambiguation to relation extraction. [Internet] [Thesis]. University of Texas – Austin; 2007. [cited 2019 Apr 19]. Available from: http://hdl.handle.net/2152/3200.

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

Council of Science Editors:

Bunescu, Razvan Constantin 1. Learning for information extraction: from named entity recognition and disambiguation to relation extraction. [Thesis]. University of Texas – Austin; 2007. Available from: http://hdl.handle.net/2152/3200

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


University of Texas – Austin

27. Kate, Rohit Jaivant, 1978-. Learning for semantic parsing with kernels under various forms of supervision.

Degree: Computer Sciences, 2007, University of Texas – Austin

 Semantic parsing involves deep semantic analysis that maps natural language sentences to their formal executable meaning representations. This is a challenging problem and is critical… (more)

Subjects/Keywords: Parsing (Computer grammar); Machine learning; Natural language processing (Computer science)

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APA (6th Edition):

Kate, Rohit Jaivant, 1. (2007). Learning for semantic parsing with kernels under various forms of supervision. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/3272

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):

Kate, Rohit Jaivant, 1978-. “Learning for semantic parsing with kernels under various forms of supervision.” 2007. Thesis, University of Texas – Austin. Accessed April 19, 2019. http://hdl.handle.net/2152/3272.

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

MLA Handbook (7th Edition):

Kate, Rohit Jaivant, 1978-. “Learning for semantic parsing with kernels under various forms of supervision.” 2007. Web. 19 Apr 2019.

Vancouver:

Kate, Rohit Jaivant 1. Learning for semantic parsing with kernels under various forms of supervision. [Internet] [Thesis]. University of Texas – Austin; 2007. [cited 2019 Apr 19]. Available from: http://hdl.handle.net/2152/3272.

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

Council of Science Editors:

Kate, Rohit Jaivant 1. Learning for semantic parsing with kernels under various forms of supervision. [Thesis]. University of Texas – Austin; 2007. Available from: http://hdl.handle.net/2152/3272

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


University of Texas – Austin

28. Wong, Yuk Wah, 1979-. Learning for semantic parsing and natural language generation using statistical machine translation techniques.

Degree: Computer Sciences, 2007, University of Texas – Austin

 One of the main goals of natural language processing (NLP) is to build au- tomated systems that can understand and generate human lanugages. This goal… (more)

Subjects/Keywords: Natural language processing (Computer science); Parsing (Computer grammar)

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

APA (6th Edition):

Wong, Yuk Wah, 1. (2007). Learning for semantic parsing and natural language generation using statistical machine translation techniques. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/3351

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):

Wong, Yuk Wah, 1979-. “Learning for semantic parsing and natural language generation using statistical machine translation techniques.” 2007. Thesis, University of Texas – Austin. Accessed April 19, 2019. http://hdl.handle.net/2152/3351.

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

MLA Handbook (7th Edition):

Wong, Yuk Wah, 1979-. “Learning for semantic parsing and natural language generation using statistical machine translation techniques.” 2007. Web. 19 Apr 2019.

Vancouver:

Wong, Yuk Wah 1. Learning for semantic parsing and natural language generation using statistical machine translation techniques. [Internet] [Thesis]. University of Texas – Austin; 2007. [cited 2019 Apr 19]. Available from: http://hdl.handle.net/2152/3351.

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

Council of Science Editors:

Wong, Yuk Wah 1. Learning for semantic parsing and natural language generation using statistical machine translation techniques. [Thesis]. University of Texas – Austin; 2007. Available from: http://hdl.handle.net/2152/3351

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


University of Texas – Austin

29. Basu, Sugato. Semi-supervised clustering: probabilistic models, algorithms and experiments.

Degree: Computer Sciences, 2005, University of Texas – Austin

 Clustering is one of the most common data mining tasks, used frequently for data categorization and analysis in both industry and academia. The focus of… (more)

Subjects/Keywords: Cluster analysis; Computer algorithms

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

APA (6th Edition):

Basu, S. (2005). Semi-supervised clustering: probabilistic models, algorithms and experiments. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/1820

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):

Basu, Sugato. “Semi-supervised clustering: probabilistic models, algorithms and experiments.” 2005. Thesis, University of Texas – Austin. Accessed April 19, 2019. http://hdl.handle.net/2152/1820.

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

MLA Handbook (7th Edition):

Basu, Sugato. “Semi-supervised clustering: probabilistic models, algorithms and experiments.” 2005. Web. 19 Apr 2019.

Vancouver:

Basu S. Semi-supervised clustering: probabilistic models, algorithms and experiments. [Internet] [Thesis]. University of Texas – Austin; 2005. [cited 2019 Apr 19]. Available from: http://hdl.handle.net/2152/1820.

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

Council of Science Editors:

Basu S. Semi-supervised clustering: probabilistic models, algorithms and experiments. [Thesis]. University of Texas – Austin; 2005. Available from: http://hdl.handle.net/2152/1820

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


University of Texas – Austin

30. Melville, Prem Noel. Creating diverse ensemble classifiers to reduce supervision.

Degree: Computer Sciences, 2005, University of Texas – Austin

 Ensemble methods like Bagging and Boosting which combine the decisions of multiple hypotheses are some of the strongest existing machine learning methods. The diversity of… (more)

Subjects/Keywords: Machine learning

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

APA (6th Edition):

Melville, P. N. (2005). Creating diverse ensemble classifiers to reduce supervision. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/2295

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):

Melville, Prem Noel. “Creating diverse ensemble classifiers to reduce supervision.” 2005. Thesis, University of Texas – Austin. Accessed April 19, 2019. http://hdl.handle.net/2152/2295.

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

MLA Handbook (7th Edition):

Melville, Prem Noel. “Creating diverse ensemble classifiers to reduce supervision.” 2005. Web. 19 Apr 2019.

Vancouver:

Melville PN. Creating diverse ensemble classifiers to reduce supervision. [Internet] [Thesis]. University of Texas – Austin; 2005. [cited 2019 Apr 19]. Available from: http://hdl.handle.net/2152/2295.

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

Council of Science Editors:

Melville PN. Creating diverse ensemble classifiers to reduce supervision. [Thesis]. University of Texas – Austin; 2005. Available from: http://hdl.handle.net/2152/2295

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

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