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You searched for subject:(Statistical learning). Showing records 1 – 30 of 519 total matches.

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University of Notre Dame

1. WonJae Shin. Learning a Recursive Center-Embedding Rule and the Role of Test Method and Feedback</h1>.

Degree: PhD, Psychology, 2014, University of Notre Dame

  Language is a quintessential and complex human skill. One reflection is the ability to produce and understand an infinite number of new sentences (i.e.,… (more)

Subjects/Keywords: Statistical learning; Learning; Grammar; Rule learning; Recursion

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APA (6th Edition):

Shin, W. (2014). Learning a Recursive Center-Embedding Rule and the Role of Test Method and Feedback</h1>. (Doctoral Dissertation). University of Notre Dame. Retrieved from https://curate.nd.edu/show/nc580k24h9w

Chicago Manual of Style (16th Edition):

Shin, WonJae. “Learning a Recursive Center-Embedding Rule and the Role of Test Method and Feedback</h1>.” 2014. Doctoral Dissertation, University of Notre Dame. Accessed May 22, 2019. https://curate.nd.edu/show/nc580k24h9w.

MLA Handbook (7th Edition):

Shin, WonJae. “Learning a Recursive Center-Embedding Rule and the Role of Test Method and Feedback</h1>.” 2014. Web. 22 May 2019.

Vancouver:

Shin W. Learning a Recursive Center-Embedding Rule and the Role of Test Method and Feedback</h1>. [Internet] [Doctoral dissertation]. University of Notre Dame; 2014. [cited 2019 May 22]. Available from: https://curate.nd.edu/show/nc580k24h9w.

Council of Science Editors:

Shin W. Learning a Recursive Center-Embedding Rule and the Role of Test Method and Feedback</h1>. [Doctoral Dissertation]. University of Notre Dame; 2014. Available from: https://curate.nd.edu/show/nc580k24h9w


University of Texas – Austin

2. -1401-7917. Machine learning phases in statistical physics.

Degree: Statistics, 2017, University of Texas – Austin

 Conventionally, the study of phases in statistical mechan- ics is performed with the help of random sampling tools. Among the most powerful are Monte Carlo… (more)

Subjects/Keywords: Machine learning; Statistical physics

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APA (6th Edition):

-1401-7917. (2017). Machine learning phases in statistical physics. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/63803

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):

-1401-7917. “Machine learning phases in statistical physics.” 2017. Thesis, University of Texas – Austin. Accessed May 22, 2019. http://hdl.handle.net/2152/63803.

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):

-1401-7917. “Machine learning phases in statistical physics.” 2017. Web. 22 May 2019.

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

Vancouver:

-1401-7917. Machine learning phases in statistical physics. [Internet] [Thesis]. University of Texas – Austin; 2017. [cited 2019 May 22]. Available from: http://hdl.handle.net/2152/63803.

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:

-1401-7917. Machine learning phases in statistical physics. [Thesis]. University of Texas – Austin; 2017. Available from: http://hdl.handle.net/2152/63803

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 Notre Dame

3. WonJae Shin. Learning Grammar via Statistical Mechanism</h1>.

Degree: MA, Psychology, 2013, University of Notre Dame

  A novel word-by-word prediction paradigm, which employs a task similar to the one given to Elman’s (1990, 1991, 1993) simple recurrent networks, is used… (more)

Subjects/Keywords: artificial grammar learning; statistical mechanism

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APA (6th Edition):

Shin, W. (2013). Learning Grammar via Statistical Mechanism</h1>. (Masters Thesis). University of Notre Dame. Retrieved from https://curate.nd.edu/show/v979v12173x

Chicago Manual of Style (16th Edition):

Shin, WonJae. “Learning Grammar via Statistical Mechanism</h1>.” 2013. Masters Thesis, University of Notre Dame. Accessed May 22, 2019. https://curate.nd.edu/show/v979v12173x.

MLA Handbook (7th Edition):

Shin, WonJae. “Learning Grammar via Statistical Mechanism</h1>.” 2013. Web. 22 May 2019.

Vancouver:

Shin W. Learning Grammar via Statistical Mechanism</h1>. [Internet] [Masters thesis]. University of Notre Dame; 2013. [cited 2019 May 22]. Available from: https://curate.nd.edu/show/v979v12173x.

Council of Science Editors:

Shin W. Learning Grammar via Statistical Mechanism</h1>. [Masters Thesis]. University of Notre Dame; 2013. Available from: https://curate.nd.edu/show/v979v12173x


Boston University

4. Day, Alexandre. An application of machine learning to statistical physics: from the phases of quantum control to satisfiability problems.

Degree: PhD, Physics, 2019, Boston University

 This dissertation presents a study of machine learning methods with a focus on applications to statistical and condensed matter physics, in particular the problem of… (more)

Subjects/Keywords: Physics; Machine learning; Statistical physics

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APA (6th Edition):

Day, A. (2019). An application of machine learning to statistical physics: from the phases of quantum control to satisfiability problems. (Doctoral Dissertation). Boston University. Retrieved from http://hdl.handle.net/2144/34908

Chicago Manual of Style (16th Edition):

Day, Alexandre. “An application of machine learning to statistical physics: from the phases of quantum control to satisfiability problems.” 2019. Doctoral Dissertation, Boston University. Accessed May 22, 2019. http://hdl.handle.net/2144/34908.

MLA Handbook (7th Edition):

Day, Alexandre. “An application of machine learning to statistical physics: from the phases of quantum control to satisfiability problems.” 2019. Web. 22 May 2019.

Vancouver:

Day A. An application of machine learning to statistical physics: from the phases of quantum control to satisfiability problems. [Internet] [Doctoral dissertation]. Boston University; 2019. [cited 2019 May 22]. Available from: http://hdl.handle.net/2144/34908.

Council of Science Editors:

Day A. An application of machine learning to statistical physics: from the phases of quantum control to satisfiability problems. [Doctoral Dissertation]. Boston University; 2019. Available from: http://hdl.handle.net/2144/34908


Universiteit Utrecht

5. Leeuwen, D.M. van. Statistical Learning and Rule-based Learning in Bilinguals.

Degree: 2010, Universiteit Utrecht

 This thesis investigates the differences between monolingual and simultaneous bilingual adults' statistical and rule-based learning abilities. The question it sought to answer is if bilinguals… (more)

Subjects/Keywords: Letteren; bilingualism; statistical learning; rule-based learning; segmentation; artifical language learning

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APA (6th Edition):

Leeuwen, D. M. v. (2010). Statistical Learning and Rule-based Learning in Bilinguals. (Masters Thesis). Universiteit Utrecht. Retrieved from http://dspace.library.uu.nl:8080/handle/1874/179588

Chicago Manual of Style (16th Edition):

Leeuwen, D M van. “Statistical Learning and Rule-based Learning in Bilinguals.” 2010. Masters Thesis, Universiteit Utrecht. Accessed May 22, 2019. http://dspace.library.uu.nl:8080/handle/1874/179588.

MLA Handbook (7th Edition):

Leeuwen, D M van. “Statistical Learning and Rule-based Learning in Bilinguals.” 2010. Web. 22 May 2019.

Vancouver:

Leeuwen DMv. Statistical Learning and Rule-based Learning in Bilinguals. [Internet] [Masters thesis]. Universiteit Utrecht; 2010. [cited 2019 May 22]. Available from: http://dspace.library.uu.nl:8080/handle/1874/179588.

Council of Science Editors:

Leeuwen DMv. Statistical Learning and Rule-based Learning in Bilinguals. [Masters Thesis]. Universiteit Utrecht; 2010. Available from: http://dspace.library.uu.nl:8080/handle/1874/179588


Northeastern University

6. Shaker, Matineh. Manifold learning and unwrapping using density ridges.

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

 Manifold learning is used for determining a coordinate system for high dimensional data on its intrinsic low-dimensional manifold, in order to (approximately) unwrap the manifold… (more)

Subjects/Keywords: dimensionality reduction; machine learning; manifold learning; sparse learning; statistical modeling

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APA (6th Edition):

Shaker, M. (2016). Manifold learning and unwrapping using density ridges. (Doctoral Dissertation). Northeastern University. Retrieved from http://hdl.handle.net/2047/D20260369

Chicago Manual of Style (16th Edition):

Shaker, Matineh. “Manifold learning and unwrapping using density ridges.” 2016. Doctoral Dissertation, Northeastern University. Accessed May 22, 2019. http://hdl.handle.net/2047/D20260369.

MLA Handbook (7th Edition):

Shaker, Matineh. “Manifold learning and unwrapping using density ridges.” 2016. Web. 22 May 2019.

Vancouver:

Shaker M. Manifold learning and unwrapping using density ridges. [Internet] [Doctoral dissertation]. Northeastern University; 2016. [cited 2019 May 22]. Available from: http://hdl.handle.net/2047/D20260369.

Council of Science Editors:

Shaker M. Manifold learning and unwrapping using density ridges. [Doctoral Dissertation]. Northeastern University; 2016. Available from: http://hdl.handle.net/2047/D20260369


University of Waterloo

7. Lu, Tyler (Tian). Fundamental Limitations of Semi-Supervised Learning.

Degree: 2009, University of Waterloo

 The emergence of a new paradigm in machine learning known as semi-supervised learning (SSL) has seen benefits to many applications where labeled data is expensive… (more)

Subjects/Keywords: artificial intelligence; machine learning; semi-supervised learning; statistical learning theory

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APA (6th Edition):

Lu, T. (. (2009). Fundamental Limitations of Semi-Supervised Learning. (Thesis). University of Waterloo. Retrieved from http://hdl.handle.net/10012/4387

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):

Lu, Tyler (Tian). “Fundamental Limitations of Semi-Supervised Learning.” 2009. Thesis, University of Waterloo. Accessed May 22, 2019. http://hdl.handle.net/10012/4387.

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

MLA Handbook (7th Edition):

Lu, Tyler (Tian). “Fundamental Limitations of Semi-Supervised Learning.” 2009. Web. 22 May 2019.

Vancouver:

Lu T(. Fundamental Limitations of Semi-Supervised Learning. [Internet] [Thesis]. University of Waterloo; 2009. [cited 2019 May 22]. Available from: http://hdl.handle.net/10012/4387.

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

Council of Science Editors:

Lu T(. Fundamental Limitations of Semi-Supervised Learning. [Thesis]. University of Waterloo; 2009. Available from: http://hdl.handle.net/10012/4387

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


University of Edinburgh

8. Warren, Mariah. Modeling Allophonic Rule Learning with Distributional and Phonetic Factors.

Degree: 2012, University of Edinburgh

 A fundamental task faced by infants during language acquisition is acquiring the phonological structure of the native language, including the abstract phonemic categories and the… (more)

Subjects/Keywords: Allophone; Phonological Rules; Statistical Learning; Computational Modeling

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APA (6th Edition):

Warren, M. (2012). Modeling Allophonic Rule Learning with Distributional and Phonetic Factors. (Thesis). University of Edinburgh. Retrieved from http://hdl.handle.net/1842/8496

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):

Warren, Mariah. “Modeling Allophonic Rule Learning with Distributional and Phonetic Factors.” 2012. Thesis, University of Edinburgh. Accessed May 22, 2019. http://hdl.handle.net/1842/8496.

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

MLA Handbook (7th Edition):

Warren, Mariah. “Modeling Allophonic Rule Learning with Distributional and Phonetic Factors.” 2012. Web. 22 May 2019.

Vancouver:

Warren M. Modeling Allophonic Rule Learning with Distributional and Phonetic Factors. [Internet] [Thesis]. University of Edinburgh; 2012. [cited 2019 May 22]. Available from: http://hdl.handle.net/1842/8496.

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

Council of Science Editors:

Warren M. Modeling Allophonic Rule Learning with Distributional and Phonetic Factors. [Thesis]. University of Edinburgh; 2012. Available from: http://hdl.handle.net/1842/8496

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


Cornell University

9. Misyak, Jennifer. Empirically Bridging Individual Differences Across Statistical Learning And Language .

Degree: 2012, Cornell University

Statistical learning-the process of extracting patterns from distributional properties of the input-has been proposed as a key mechanism for acquiring knowledge of the probabilistic dependencies… (more)

Subjects/Keywords: statistical learning; language processing; individual differences

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APA (6th Edition):

Misyak, J. (2012). Empirically Bridging Individual Differences Across Statistical Learning And Language . (Thesis). Cornell University. Retrieved from http://hdl.handle.net/1813/31052

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):

Misyak, Jennifer. “Empirically Bridging Individual Differences Across Statistical Learning And Language .” 2012. Thesis, Cornell University. Accessed May 22, 2019. http://hdl.handle.net/1813/31052.

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

MLA Handbook (7th Edition):

Misyak, Jennifer. “Empirically Bridging Individual Differences Across Statistical Learning And Language .” 2012. Web. 22 May 2019.

Vancouver:

Misyak J. Empirically Bridging Individual Differences Across Statistical Learning And Language . [Internet] [Thesis]. Cornell University; 2012. [cited 2019 May 22]. Available from: http://hdl.handle.net/1813/31052.

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

Council of Science Editors:

Misyak J. Empirically Bridging Individual Differences Across Statistical Learning And Language . [Thesis]. Cornell University; 2012. Available from: http://hdl.handle.net/1813/31052

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


Penn State University

10. Mitchel, Aaron Daniell. Cross-Modal Effects in Statistical Learning.

Degree: PhD, Psychology, 2010, Penn State University

 A central question of research on language acquisition concerns which types of information in the environmental input are available to language learners, as well as… (more)

Subjects/Keywords: multimodal integration; statistical learning; language acquisition

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APA (6th Edition):

Mitchel, A. D. (2010). Cross-Modal Effects in Statistical Learning. (Doctoral Dissertation). Penn State University. Retrieved from https://etda.libraries.psu.edu/catalog/11637

Chicago Manual of Style (16th Edition):

Mitchel, Aaron Daniell. “Cross-Modal Effects in Statistical Learning.” 2010. Doctoral Dissertation, Penn State University. Accessed May 22, 2019. https://etda.libraries.psu.edu/catalog/11637.

MLA Handbook (7th Edition):

Mitchel, Aaron Daniell. “Cross-Modal Effects in Statistical Learning.” 2010. Web. 22 May 2019.

Vancouver:

Mitchel AD. Cross-Modal Effects in Statistical Learning. [Internet] [Doctoral dissertation]. Penn State University; 2010. [cited 2019 May 22]. Available from: https://etda.libraries.psu.edu/catalog/11637.

Council of Science Editors:

Mitchel AD. Cross-Modal Effects in Statistical Learning. [Doctoral Dissertation]. Penn State University; 2010. Available from: https://etda.libraries.psu.edu/catalog/11637


University of California – Berkeley

11. Wilson, Sarah Thomas. Statistical Learning of Syntax (Among Other Higher-Order Relational Structures).

Degree: Psychology, 2011, University of California – Berkeley

 Fluency in a language requires understanding abstract relationships between types or classes of words - the syntax of language. The learning problem has seemed so… (more)

Subjects/Keywords: Psychology; language acquisition; statistical learning; syntax

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APA (6th Edition):

Wilson, S. T. (2011). Statistical Learning of Syntax (Among Other Higher-Order Relational Structures). (Thesis). University of California – Berkeley. Retrieved from http://www.escholarship.org/uc/item/28162577

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):

Wilson, Sarah Thomas. “Statistical Learning of Syntax (Among Other Higher-Order Relational Structures).” 2011. Thesis, University of California – Berkeley. Accessed May 22, 2019. http://www.escholarship.org/uc/item/28162577.

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

MLA Handbook (7th Edition):

Wilson, Sarah Thomas. “Statistical Learning of Syntax (Among Other Higher-Order Relational Structures).” 2011. Web. 22 May 2019.

Vancouver:

Wilson ST. Statistical Learning of Syntax (Among Other Higher-Order Relational Structures). [Internet] [Thesis]. University of California – Berkeley; 2011. [cited 2019 May 22]. Available from: http://www.escholarship.org/uc/item/28162577.

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

Council of Science Editors:

Wilson ST. Statistical Learning of Syntax (Among Other Higher-Order Relational Structures). [Thesis]. University of California – Berkeley; 2011. Available from: http://www.escholarship.org/uc/item/28162577

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


University of Kansas

12. Zhong, Yi. Feature selection and classification for high-dimensional biological data under cross-validation framework.

Degree: PhD, Biostatistics, 2018, University of Kansas

 This research focuses on using statistical learning methods on high-dimensional biological data analysis. In our implementation of high-dimensional biological data analysis, we primarily utilize the… (more)

Subjects/Keywords: Statistics; cross-validation; feature selection; statistical learning

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APA (6th Edition):

Zhong, Y. (2018). Feature selection and classification for high-dimensional biological data under cross-validation framework. (Doctoral Dissertation). University of Kansas. Retrieved from http://hdl.handle.net/1808/27072

Chicago Manual of Style (16th Edition):

Zhong, Yi. “Feature selection and classification for high-dimensional biological data under cross-validation framework.” 2018. Doctoral Dissertation, University of Kansas. Accessed May 22, 2019. http://hdl.handle.net/1808/27072.

MLA Handbook (7th Edition):

Zhong, Yi. “Feature selection and classification for high-dimensional biological data under cross-validation framework.” 2018. Web. 22 May 2019.

Vancouver:

Zhong Y. Feature selection and classification for high-dimensional biological data under cross-validation framework. [Internet] [Doctoral dissertation]. University of Kansas; 2018. [cited 2019 May 22]. Available from: http://hdl.handle.net/1808/27072.

Council of Science Editors:

Zhong Y. Feature selection and classification for high-dimensional biological data under cross-validation framework. [Doctoral Dissertation]. University of Kansas; 2018. Available from: http://hdl.handle.net/1808/27072


Montana Tech

13. Chandler-Pepelnjak, John Winston. Modeling Conversions in Online Advertising.

Degree: PhD, 2010, Montana Tech

  This work investigates online purchasers and how to predict such sales. Advertising as a field has long been required to pay for itself – money… (more)

Subjects/Keywords: Clustering; Hazard Models; Online Marketing; Statistical Learning

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APA (6th Edition):

Chandler-Pepelnjak, J. W. (2010). Modeling Conversions in Online Advertising. (Doctoral Dissertation). Montana Tech. Retrieved from https://scholarworks.umt.edu/etd/670

Chicago Manual of Style (16th Edition):

Chandler-Pepelnjak, John Winston. “Modeling Conversions in Online Advertising.” 2010. Doctoral Dissertation, Montana Tech. Accessed May 22, 2019. https://scholarworks.umt.edu/etd/670.

MLA Handbook (7th Edition):

Chandler-Pepelnjak, John Winston. “Modeling Conversions in Online Advertising.” 2010. Web. 22 May 2019.

Vancouver:

Chandler-Pepelnjak JW. Modeling Conversions in Online Advertising. [Internet] [Doctoral dissertation]. Montana Tech; 2010. [cited 2019 May 22]. Available from: https://scholarworks.umt.edu/etd/670.

Council of Science Editors:

Chandler-Pepelnjak JW. Modeling Conversions in Online Advertising. [Doctoral Dissertation]. Montana Tech; 2010. Available from: https://scholarworks.umt.edu/etd/670


McMaster University

14. Pathmanathan, Thinesh. Dimension Reduction and Clustering of High Dimensional Data using a Mixture of Generalized Hyperbolic Distributions.

Degree: MSc, 2018, McMaster University

Model-based clustering is a probabilistic approach that views each cluster as a component in an appropriate mixture model. The Gaussian mixture model is one of… (more)

Subjects/Keywords: Model-based clustering; dimension reduction; statistical learning

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APA (6th Edition):

Pathmanathan, T. (2018). Dimension Reduction and Clustering of High Dimensional Data using a Mixture of Generalized Hyperbolic Distributions. (Masters Thesis). McMaster University. Retrieved from http://hdl.handle.net/11375/22758

Chicago Manual of Style (16th Edition):

Pathmanathan, Thinesh. “Dimension Reduction and Clustering of High Dimensional Data using a Mixture of Generalized Hyperbolic Distributions.” 2018. Masters Thesis, McMaster University. Accessed May 22, 2019. http://hdl.handle.net/11375/22758.

MLA Handbook (7th Edition):

Pathmanathan, Thinesh. “Dimension Reduction and Clustering of High Dimensional Data using a Mixture of Generalized Hyperbolic Distributions.” 2018. Web. 22 May 2019.

Vancouver:

Pathmanathan T. Dimension Reduction and Clustering of High Dimensional Data using a Mixture of Generalized Hyperbolic Distributions. [Internet] [Masters thesis]. McMaster University; 2018. [cited 2019 May 22]. Available from: http://hdl.handle.net/11375/22758.

Council of Science Editors:

Pathmanathan T. Dimension Reduction and Clustering of High Dimensional Data using a Mixture of Generalized Hyperbolic Distributions. [Masters Thesis]. McMaster University; 2018. Available from: http://hdl.handle.net/11375/22758


Hong Kong University of Science and Technology

15. Wang, Hao. Bayesian deep learning for integrated intelligence : bridging the gap between perception and inference.

Degree: 2017, Hong Kong University of Science and Technology

 While perception tasks such as visual object recognition and text understanding play an important role in human intelligence, the subsequent tasks that involve inference, reasoning,… (more)

Subjects/Keywords: Machine learning; Bayesian statistical decision theory

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APA (6th Edition):

Wang, H. (2017). Bayesian deep learning for integrated intelligence : bridging the gap between perception and inference. (Thesis). Hong Kong University of Science and Technology. Retrieved from https://doi.org/10.14711/thesis-991012554564003412 ; http://repository.ust.hk/ir/bitstream/1783.1-91073/1/th_redirect.html

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

Chicago Manual of Style (16th Edition):

Wang, Hao. “Bayesian deep learning for integrated intelligence : bridging the gap between perception and inference.” 2017. Thesis, Hong Kong University of Science and Technology. Accessed May 22, 2019. https://doi.org/10.14711/thesis-991012554564003412 ; http://repository.ust.hk/ir/bitstream/1783.1-91073/1/th_redirect.html.

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

MLA Handbook (7th Edition):

Wang, Hao. “Bayesian deep learning for integrated intelligence : bridging the gap between perception and inference.” 2017. Web. 22 May 2019.

Vancouver:

Wang H. Bayesian deep learning for integrated intelligence : bridging the gap between perception and inference. [Internet] [Thesis]. Hong Kong University of Science and Technology; 2017. [cited 2019 May 22]. Available from: https://doi.org/10.14711/thesis-991012554564003412 ; http://repository.ust.hk/ir/bitstream/1783.1-91073/1/th_redirect.html.

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

Council of Science Editors:

Wang H. Bayesian deep learning for integrated intelligence : bridging the gap between perception and inference. [Thesis]. Hong Kong University of Science and Technology; 2017. Available from: https://doi.org/10.14711/thesis-991012554564003412 ; http://repository.ust.hk/ir/bitstream/1783.1-91073/1/th_redirect.html

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


Cornell University

16. Gilson, Claudia. In And Out Of Context: Effects Of Visual Experience And Visual Variety On Scene Perception .

Degree: 2013, Cornell University

 Visual context is the distillation of visual experience that enables reasonable accuracy in interpreting the natural world. Context consists of all the constraints that limit… (more)

Subjects/Keywords: visual context; statistical learning; scene perception

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APA (6th Edition):

Gilson, C. (2013). In And Out Of Context: Effects Of Visual Experience And Visual Variety On Scene Perception . (Thesis). Cornell University. Retrieved from http://hdl.handle.net/1813/34214

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):

Gilson, Claudia. “In And Out Of Context: Effects Of Visual Experience And Visual Variety On Scene Perception .” 2013. Thesis, Cornell University. Accessed May 22, 2019. http://hdl.handle.net/1813/34214.

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

MLA Handbook (7th Edition):

Gilson, Claudia. “In And Out Of Context: Effects Of Visual Experience And Visual Variety On Scene Perception .” 2013. Web. 22 May 2019.

Vancouver:

Gilson C. In And Out Of Context: Effects Of Visual Experience And Visual Variety On Scene Perception . [Internet] [Thesis]. Cornell University; 2013. [cited 2019 May 22]. Available from: http://hdl.handle.net/1813/34214.

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

Council of Science Editors:

Gilson C. In And Out Of Context: Effects Of Visual Experience And Visual Variety On Scene Perception . [Thesis]. Cornell University; 2013. Available from: http://hdl.handle.net/1813/34214

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


University of Waterloo

17. Emtenan, Ariq. MachineFlow: A Web Tool to Adaptively Select EDA Tool Parameters for Digital ICs Using Statistical Learning.

Degree: 2016, University of Waterloo

 Digital integrated circuits (ICs) are the driving force behind computing, communication and entertainment in today’s world. More powerful and energy efficient ICs continue to be… (more)

Subjects/Keywords: Digital integrated circuits; physical design; statistical learning

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

APA (6th Edition):

Emtenan, A. (2016). MachineFlow: A Web Tool to Adaptively Select EDA Tool Parameters for Digital ICs Using Statistical Learning. (Thesis). University of Waterloo. Retrieved from http://hdl.handle.net/10012/11027

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):

Emtenan, Ariq. “MachineFlow: A Web Tool to Adaptively Select EDA Tool Parameters for Digital ICs Using Statistical Learning.” 2016. Thesis, University of Waterloo. Accessed May 22, 2019. http://hdl.handle.net/10012/11027.

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

MLA Handbook (7th Edition):

Emtenan, Ariq. “MachineFlow: A Web Tool to Adaptively Select EDA Tool Parameters for Digital ICs Using Statistical Learning.” 2016. Web. 22 May 2019.

Vancouver:

Emtenan A. MachineFlow: A Web Tool to Adaptively Select EDA Tool Parameters for Digital ICs Using Statistical Learning. [Internet] [Thesis]. University of Waterloo; 2016. [cited 2019 May 22]. Available from: http://hdl.handle.net/10012/11027.

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

Council of Science Editors:

Emtenan A. MachineFlow: A Web Tool to Adaptively Select EDA Tool Parameters for Digital ICs Using Statistical Learning. [Thesis]. University of Waterloo; 2016. Available from: http://hdl.handle.net/10012/11027

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


University of Ottawa

18. Tsui, Sin Mei. Statistical Learning in a Bilingual Environment .

Degree: 2018, University of Ottawa

Statistical learning refers to the ability to track regular patterns in sensory input from ambient environments. This learning mechanism can exploit a wide range of… (more)

Subjects/Keywords: Psychology; Statistical learning; Word segmentation; Bilingualism

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

APA (6th Edition):

Tsui, S. M. (2018). Statistical Learning in a Bilingual Environment . (Thesis). University of Ottawa. Retrieved from http://hdl.handle.net/10393/38048

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):

Tsui, Sin Mei. “Statistical Learning in a Bilingual Environment .” 2018. Thesis, University of Ottawa. Accessed May 22, 2019. http://hdl.handle.net/10393/38048.

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

MLA Handbook (7th Edition):

Tsui, Sin Mei. “Statistical Learning in a Bilingual Environment .” 2018. Web. 22 May 2019.

Vancouver:

Tsui SM. Statistical Learning in a Bilingual Environment . [Internet] [Thesis]. University of Ottawa; 2018. [cited 2019 May 22]. Available from: http://hdl.handle.net/10393/38048.

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

Council of Science Editors:

Tsui SM. Statistical Learning in a Bilingual Environment . [Thesis]. University of Ottawa; 2018. Available from: http://hdl.handle.net/10393/38048

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


University of New South Wales

19. Liu, Xianghang. New Algorithms for Graphical Models and Their Applications in Learning.

Degree: Computer Science & Engineering, 2015, University of New South Wales

 Probabilistic graphical models bring together graph theory and probability theory in a powerful formalism for multivariate statistical modelling. Since many machine learning problems involve the… (more)

Subjects/Keywords: statistical inference; machine learning; graphical model

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

APA (6th Edition):

Liu, X. (2015). New Algorithms for Graphical Models and Their Applications in Learning. (Doctoral Dissertation). University of New South Wales. Retrieved from http://handle.unsw.edu.au/1959.4/55080 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:36494/SOURCE02?view=true

Chicago Manual of Style (16th Edition):

Liu, Xianghang. “New Algorithms for Graphical Models and Their Applications in Learning.” 2015. Doctoral Dissertation, University of New South Wales. Accessed May 22, 2019. http://handle.unsw.edu.au/1959.4/55080 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:36494/SOURCE02?view=true.

MLA Handbook (7th Edition):

Liu, Xianghang. “New Algorithms for Graphical Models and Their Applications in Learning.” 2015. Web. 22 May 2019.

Vancouver:

Liu X. New Algorithms for Graphical Models and Their Applications in Learning. [Internet] [Doctoral dissertation]. University of New South Wales; 2015. [cited 2019 May 22]. Available from: http://handle.unsw.edu.au/1959.4/55080 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:36494/SOURCE02?view=true.

Council of Science Editors:

Liu X. New Algorithms for Graphical Models and Their Applications in Learning. [Doctoral Dissertation]. University of New South Wales; 2015. Available from: http://handle.unsw.edu.au/1959.4/55080 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:36494/SOURCE02?view=true


Rochester Institute of Technology

20. Darlington, William J. Predicting underperformance from students in upper level engineering courses.

Degree: MS, Industrial and Systems Engineering, 2017, Rochester Institute of Technology

  Recent research in academic analytics has focused on predicting student performance within, and sometimes across courses for the purpose of informing early interventions. While… (more)

Subjects/Keywords: Analytics; Education; Learning; Performance; Statistical modeling; Student

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

APA (6th Edition):

Darlington, W. J. (2017). Predicting underperformance from students in upper level engineering courses. (Masters Thesis). Rochester Institute of Technology. Retrieved from https://scholarworks.rit.edu/theses/9549

Chicago Manual of Style (16th Edition):

Darlington, William J. “Predicting underperformance from students in upper level engineering courses.” 2017. Masters Thesis, Rochester Institute of Technology. Accessed May 22, 2019. https://scholarworks.rit.edu/theses/9549.

MLA Handbook (7th Edition):

Darlington, William J. “Predicting underperformance from students in upper level engineering courses.” 2017. Web. 22 May 2019.

Vancouver:

Darlington WJ. Predicting underperformance from students in upper level engineering courses. [Internet] [Masters thesis]. Rochester Institute of Technology; 2017. [cited 2019 May 22]. Available from: https://scholarworks.rit.edu/theses/9549.

Council of Science Editors:

Darlington WJ. Predicting underperformance from students in upper level engineering courses. [Masters Thesis]. Rochester Institute of Technology; 2017. Available from: https://scholarworks.rit.edu/theses/9549


University of Rochester

21. Fine, Alex Brabham; Jaeger, T. Florian. Prediction, error, and adaptation during online sentence comprehension.

Degree: PhD, 2013, University of Rochester

 A fundamental challenge for human cognition is perceiving and acting in a world in which the statistics that characterize available sensory data are non-stationary. This… (more)

Subjects/Keywords: Adaptation; Priming; Psycholinguistics; Sentence processing; Statistical learning

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

APA (6th Edition):

Fine, Alex Brabham; Jaeger, T. F. (2013). Prediction, error, and adaptation during online sentence comprehension. (Doctoral Dissertation). University of Rochester. Retrieved from http://hdl.handle.net/1802/27942

Chicago Manual of Style (16th Edition):

Fine, Alex Brabham; Jaeger, T Florian. “Prediction, error, and adaptation during online sentence comprehension.” 2013. Doctoral Dissertation, University of Rochester. Accessed May 22, 2019. http://hdl.handle.net/1802/27942.

MLA Handbook (7th Edition):

Fine, Alex Brabham; Jaeger, T Florian. “Prediction, error, and adaptation during online sentence comprehension.” 2013. Web. 22 May 2019.

Vancouver:

Fine, Alex Brabham; Jaeger TF. Prediction, error, and adaptation during online sentence comprehension. [Internet] [Doctoral dissertation]. University of Rochester; 2013. [cited 2019 May 22]. Available from: http://hdl.handle.net/1802/27942.

Council of Science Editors:

Fine, Alex Brabham; Jaeger TF. Prediction, error, and adaptation during online sentence comprehension. [Doctoral Dissertation]. University of Rochester; 2013. Available from: http://hdl.handle.net/1802/27942


University of Rochester

22. Fine, Alex Brabham; Jaeger, T. Florian. Prediction, error, and adaptation during online sentence comprehension.

Degree: PhD, 2013, University of Rochester

 A fundamental challenge for human cognition is perceiving and acting in a world in which the statistics that characterize available sensory data are non-stationary. This… (more)

Subjects/Keywords: Adaptation; Priming; Psycholinguistics; Sentence processing; Statistical learning

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

APA (6th Edition):

Fine, Alex Brabham; Jaeger, T. F. (2013). Prediction, error, and adaptation during online sentence comprehension. (Doctoral Dissertation). University of Rochester. Retrieved from http://hdl.handle.net/1802/27943

Chicago Manual of Style (16th Edition):

Fine, Alex Brabham; Jaeger, T Florian. “Prediction, error, and adaptation during online sentence comprehension.” 2013. Doctoral Dissertation, University of Rochester. Accessed May 22, 2019. http://hdl.handle.net/1802/27943.

MLA Handbook (7th Edition):

Fine, Alex Brabham; Jaeger, T Florian. “Prediction, error, and adaptation during online sentence comprehension.” 2013. Web. 22 May 2019.

Vancouver:

Fine, Alex Brabham; Jaeger TF. Prediction, error, and adaptation during online sentence comprehension. [Internet] [Doctoral dissertation]. University of Rochester; 2013. [cited 2019 May 22]. Available from: http://hdl.handle.net/1802/27943.

Council of Science Editors:

Fine, Alex Brabham; Jaeger TF. Prediction, error, and adaptation during online sentence comprehension. [Doctoral Dissertation]. University of Rochester; 2013. Available from: http://hdl.handle.net/1802/27943


University of Plymouth

23. Long, Jintao. Discovering biomarkers of Alzheimer's disease by statistical learning approaches.

Degree: PhD, 2019, University of Plymouth

 In this work, statistical learning approaches are exploited to discover biomarkers for Alzheimer's disease (AD). The contributions has been made in the fields of both… (more)

Subjects/Keywords: Bioinformatics; Statistical learning; Alzheimer's disease; Biomarker discovery

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

APA (6th Edition):

Long, J. (2019). Discovering biomarkers of Alzheimer's disease by statistical learning approaches. (Doctoral Dissertation). University of Plymouth. Retrieved from http://hdl.handle.net/10026.1/13442

Chicago Manual of Style (16th Edition):

Long, Jintao. “Discovering biomarkers of Alzheimer's disease by statistical learning approaches.” 2019. Doctoral Dissertation, University of Plymouth. Accessed May 22, 2019. http://hdl.handle.net/10026.1/13442.

MLA Handbook (7th Edition):

Long, Jintao. “Discovering biomarkers of Alzheimer's disease by statistical learning approaches.” 2019. Web. 22 May 2019.

Vancouver:

Long J. Discovering biomarkers of Alzheimer's disease by statistical learning approaches. [Internet] [Doctoral dissertation]. University of Plymouth; 2019. [cited 2019 May 22]. Available from: http://hdl.handle.net/10026.1/13442.

Council of Science Editors:

Long J. Discovering biomarkers of Alzheimer's disease by statistical learning approaches. [Doctoral Dissertation]. University of Plymouth; 2019. Available from: http://hdl.handle.net/10026.1/13442


UCLA

24. Barakat, Brandon Keith. Modulation of Implicit Sensory and Sensorimotor Learning: An Investigation Within and Across Sensory Modalities.

Degree: Psychology, 2014, UCLA

 Implicit forms of learning are what govern many aspects of human perception, cognition, and actions. This dissertation describes three studies, each of which investigated a… (more)

Subjects/Keywords: Psychology; Cognitive psychology; Neurosciences; Implicit Learning; Multisensory; Perception; Perceptual Learning; Sensorimotor Learning; Statistical Learning

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

APA (6th Edition):

Barakat, B. K. (2014). Modulation of Implicit Sensory and Sensorimotor Learning: An Investigation Within and Across Sensory Modalities. (Thesis). UCLA. Retrieved from http://www.escholarship.org/uc/item/6tj3r09s

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):

Barakat, Brandon Keith. “Modulation of Implicit Sensory and Sensorimotor Learning: An Investigation Within and Across Sensory Modalities.” 2014. Thesis, UCLA. Accessed May 22, 2019. http://www.escholarship.org/uc/item/6tj3r09s.

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

MLA Handbook (7th Edition):

Barakat, Brandon Keith. “Modulation of Implicit Sensory and Sensorimotor Learning: An Investigation Within and Across Sensory Modalities.” 2014. Web. 22 May 2019.

Vancouver:

Barakat BK. Modulation of Implicit Sensory and Sensorimotor Learning: An Investigation Within and Across Sensory Modalities. [Internet] [Thesis]. UCLA; 2014. [cited 2019 May 22]. Available from: http://www.escholarship.org/uc/item/6tj3r09s.

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

Council of Science Editors:

Barakat BK. Modulation of Implicit Sensory and Sensorimotor Learning: An Investigation Within and Across Sensory Modalities. [Thesis]. UCLA; 2014. Available from: http://www.escholarship.org/uc/item/6tj3r09s

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


University of Tennessee – Knoxville

25. Karaman, Ferhat. Incorporating Memory Processes in the Study of Early Language Acquisition.

Degree: 2018, University of Tennessee – Knoxville

 Critical to the learning of any language is the learning of the words in that language. Therefore, an extensive amount of research in language development… (more)

Subjects/Keywords: Language Acquisition; Statistical Learning; Word Learning; Word Segmentation; Infant Memory

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

APA (6th Edition):

Karaman, F. (2018). Incorporating Memory Processes in the Study of Early Language Acquisition. (Doctoral Dissertation). University of Tennessee – Knoxville. Retrieved from https://trace.tennessee.edu/utk_graddiss/5031

Chicago Manual of Style (16th Edition):

Karaman, Ferhat. “Incorporating Memory Processes in the Study of Early Language Acquisition.” 2018. Doctoral Dissertation, University of Tennessee – Knoxville. Accessed May 22, 2019. https://trace.tennessee.edu/utk_graddiss/5031.

MLA Handbook (7th Edition):

Karaman, Ferhat. “Incorporating Memory Processes in the Study of Early Language Acquisition.” 2018. Web. 22 May 2019.

Vancouver:

Karaman F. Incorporating Memory Processes in the Study of Early Language Acquisition. [Internet] [Doctoral dissertation]. University of Tennessee – Knoxville; 2018. [cited 2019 May 22]. Available from: https://trace.tennessee.edu/utk_graddiss/5031.

Council of Science Editors:

Karaman F. Incorporating Memory Processes in the Study of Early Language Acquisition. [Doctoral Dissertation]. University of Tennessee – Knoxville; 2018. Available from: https://trace.tennessee.edu/utk_graddiss/5031


McMaster University

26. Blostein, Martin. An Efficient Implementation of a Robust Clustering Algorithm.

Degree: MSc, 2016, McMaster University

Clustering and classification are fundamental problems in statistical and machine learning, with a broad range of applications. A common approach is the Gaussian mixture model,… (more)

Subjects/Keywords: clustering; classification; statistical learning; machine learning; robust; computational statistics; mixture models

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APA (6th Edition):

Blostein, M. (2016). An Efficient Implementation of a Robust Clustering Algorithm. (Masters Thesis). McMaster University. Retrieved from http://hdl.handle.net/11375/20598

Chicago Manual of Style (16th Edition):

Blostein, Martin. “An Efficient Implementation of a Robust Clustering Algorithm.” 2016. Masters Thesis, McMaster University. Accessed May 22, 2019. http://hdl.handle.net/11375/20598.

MLA Handbook (7th Edition):

Blostein, Martin. “An Efficient Implementation of a Robust Clustering Algorithm.” 2016. Web. 22 May 2019.

Vancouver:

Blostein M. An Efficient Implementation of a Robust Clustering Algorithm. [Internet] [Masters thesis]. McMaster University; 2016. [cited 2019 May 22]. Available from: http://hdl.handle.net/11375/20598.

Council of Science Editors:

Blostein M. An Efficient Implementation of a Robust Clustering Algorithm. [Masters Thesis]. McMaster University; 2016. Available from: http://hdl.handle.net/11375/20598


McMaster University

27. Matira, Kevin. Discriminant Analysis for Longitudinal Data.

Degree: MSc, 2017, McMaster University

Various approaches for discriminant analysis of longitudinal data are investigated, with some focus on model-based approaches. The latter are typically based on the modi ed… (more)

Subjects/Keywords: mixture models; supervised learning; longitudinal data; classification; statistical learning

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

APA (6th Edition):

Matira, K. (2017). Discriminant Analysis for Longitudinal Data. (Masters Thesis). McMaster University. Retrieved from http://hdl.handle.net/11375/22317

Chicago Manual of Style (16th Edition):

Matira, Kevin. “Discriminant Analysis for Longitudinal Data.” 2017. Masters Thesis, McMaster University. Accessed May 22, 2019. http://hdl.handle.net/11375/22317.

MLA Handbook (7th Edition):

Matira, Kevin. “Discriminant Analysis for Longitudinal Data.” 2017. Web. 22 May 2019.

Vancouver:

Matira K. Discriminant Analysis for Longitudinal Data. [Internet] [Masters thesis]. McMaster University; 2017. [cited 2019 May 22]. Available from: http://hdl.handle.net/11375/22317.

Council of Science Editors:

Matira K. Discriminant Analysis for Longitudinal Data. [Masters Thesis]. McMaster University; 2017. Available from: http://hdl.handle.net/11375/22317


Columbia University

28. Ma, Yuting. Flexible Sparse Learning of Feature Subspaces.

Degree: 2017, Columbia University

 It is widely observed that the performances of many traditional statistical learning methods degenerate when confronted with high-dimensional data. One promising approach to prevent this… (more)

Subjects/Keywords: Mathematical statistics; Machine learning – Statistical methods; Machine learning; Statistics

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

APA (6th Edition):

Ma, Y. (2017). Flexible Sparse Learning of Feature Subspaces. (Doctoral Dissertation). Columbia University. Retrieved from https://doi.org/10.7916/D83X8CBB

Chicago Manual of Style (16th Edition):

Ma, Yuting. “Flexible Sparse Learning of Feature Subspaces.” 2017. Doctoral Dissertation, Columbia University. Accessed May 22, 2019. https://doi.org/10.7916/D83X8CBB.

MLA Handbook (7th Edition):

Ma, Yuting. “Flexible Sparse Learning of Feature Subspaces.” 2017. Web. 22 May 2019.

Vancouver:

Ma Y. Flexible Sparse Learning of Feature Subspaces. [Internet] [Doctoral dissertation]. Columbia University; 2017. [cited 2019 May 22]. Available from: https://doi.org/10.7916/D83X8CBB.

Council of Science Editors:

Ma Y. Flexible Sparse Learning of Feature Subspaces. [Doctoral Dissertation]. Columbia University; 2017. Available from: https://doi.org/10.7916/D83X8CBB


University of Connecticut

29. Mayo, Jessica. Intact Statistical Word Learning in Autism Spectrum Disorders.

Degree: MS, Psychology, 2011, University of Connecticut

  Individuals with Autism Spectrum Disorders (ASD) have impairments in language acquisition, but the underlying mechanism of these deficits is poorly understood. Implicit learning appears… (more)

Subjects/Keywords: autism; language; implicit learning; statistical learning; speech segmentation

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APA (6th Edition):

Mayo, J. (2011). Intact Statistical Word Learning in Autism Spectrum Disorders. (Masters Thesis). University of Connecticut. Retrieved from https://opencommons.uconn.edu/gs_theses/181

Chicago Manual of Style (16th Edition):

Mayo, Jessica. “Intact Statistical Word Learning in Autism Spectrum Disorders.” 2011. Masters Thesis, University of Connecticut. Accessed May 22, 2019. https://opencommons.uconn.edu/gs_theses/181.

MLA Handbook (7th Edition):

Mayo, Jessica. “Intact Statistical Word Learning in Autism Spectrum Disorders.” 2011. Web. 22 May 2019.

Vancouver:

Mayo J. Intact Statistical Word Learning in Autism Spectrum Disorders. [Internet] [Masters thesis]. University of Connecticut; 2011. [cited 2019 May 22]. Available from: https://opencommons.uconn.edu/gs_theses/181.

Council of Science Editors:

Mayo J. Intact Statistical Word Learning in Autism Spectrum Disorders. [Masters Thesis]. University of Connecticut; 2011. Available from: https://opencommons.uconn.edu/gs_theses/181


University of Arizona

30. Dawson, Colin Reimer. "Explaining-Away" Effects in Rule-Learning: Evidence for Generative Probabilistic Inference in Infants and Adults .

Degree: 2011, University of Arizona

 The human desire to explain the world is the driving force behind our species' rich history of scientific and technological advancement. The ability of successive… (more)

Subjects/Keywords: Bayesian Modeling; Infant Cognition; Language Learning; Music Cognition; Statistical Learning

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APA (6th Edition):

Dawson, C. R. (2011). "Explaining-Away" Effects in Rule-Learning: Evidence for Generative Probabilistic Inference in Infants and Adults . (Doctoral Dissertation). University of Arizona. Retrieved from http://hdl.handle.net/10150/145270

Chicago Manual of Style (16th Edition):

Dawson, Colin Reimer. “"Explaining-Away" Effects in Rule-Learning: Evidence for Generative Probabilistic Inference in Infants and Adults .” 2011. Doctoral Dissertation, University of Arizona. Accessed May 22, 2019. http://hdl.handle.net/10150/145270.

MLA Handbook (7th Edition):

Dawson, Colin Reimer. “"Explaining-Away" Effects in Rule-Learning: Evidence for Generative Probabilistic Inference in Infants and Adults .” 2011. Web. 22 May 2019.

Vancouver:

Dawson CR. "Explaining-Away" Effects in Rule-Learning: Evidence for Generative Probabilistic Inference in Infants and Adults . [Internet] [Doctoral dissertation]. University of Arizona; 2011. [cited 2019 May 22]. Available from: http://hdl.handle.net/10150/145270.

Council of Science Editors:

Dawson CR. "Explaining-Away" Effects in Rule-Learning: Evidence for Generative Probabilistic Inference in Infants and Adults . [Doctoral Dissertation]. University of Arizona; 2011. Available from: http://hdl.handle.net/10150/145270

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