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You searched for subject:(Statistical machine learning). Showing records 1 – 30 of 268 total matches.

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Boston University

1. 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 November 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 Nov 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 Nov 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


University of Oxford

2. Rukat, Tammo. Logical factorisation machines : probabilistic Boolean factor models for binary data.

Degree: PhD, 2018, University of Oxford

 Logical Factorisation Machines (LFMs) are a class of latent feature models, that aim to decompose binary matrices, tensors or higher-arity relations into an approximate logical… (more)

Subjects/Keywords: Machine learning – Statistical methods

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APA (6th Edition):

Rukat, T. (2018). Logical factorisation machines : probabilistic Boolean factor models for binary data. (Doctoral Dissertation). University of Oxford. Retrieved from http://ora.ox.ac.uk/objects/uuid:9d856435-7fcb-4d27-8a46-090a35ef79ee ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.780609

Chicago Manual of Style (16th Edition):

Rukat, Tammo. “Logical factorisation machines : probabilistic Boolean factor models for binary data.” 2018. Doctoral Dissertation, University of Oxford. Accessed November 22, 2019. http://ora.ox.ac.uk/objects/uuid:9d856435-7fcb-4d27-8a46-090a35ef79ee ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.780609.

MLA Handbook (7th Edition):

Rukat, Tammo. “Logical factorisation machines : probabilistic Boolean factor models for binary data.” 2018. Web. 22 Nov 2019.

Vancouver:

Rukat T. Logical factorisation machines : probabilistic Boolean factor models for binary data. [Internet] [Doctoral dissertation]. University of Oxford; 2018. [cited 2019 Nov 22]. Available from: http://ora.ox.ac.uk/objects/uuid:9d856435-7fcb-4d27-8a46-090a35ef79ee ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.780609.

Council of Science Editors:

Rukat T. Logical factorisation machines : probabilistic Boolean factor models for binary data. [Doctoral Dissertation]. University of Oxford; 2018. Available from: http://ora.ox.ac.uk/objects/uuid:9d856435-7fcb-4d27-8a46-090a35ef79ee ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.780609


University of Texas – Austin

3. -1401-7917. Machine learning phases in statistical physics.

Degree: MSin Statistics, 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. (Masters 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

Chicago Manual of Style (16th Edition):

-1401-7917. “Machine learning phases in statistical physics.” 2017. Masters Thesis, University of Texas – Austin. Accessed November 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

MLA Handbook (7th Edition):

-1401-7917. “Machine learning phases in statistical physics.” 2017. Web. 22 Nov 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] [Masters thesis]. University of Texas – Austin; 2017. [cited 2019 Nov 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

Council of Science Editors:

-1401-7917. Machine learning phases in statistical physics. [Masters 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


Dublin City University

4. Almaghout, Hala. CCG-augmented hierarchical phrase-based statistical machine translation.

Degree: School of Computing, 2013, Dublin City University

 Augmenting Statistical Machine Translation (SMT) systems with syntactic information aims at improving translation quality. Hierarchical Phrase-Based (HPB) SMT takes a step toward incorporating syntax in… (more)

Subjects/Keywords: Machine translating; Machine learning; Statistical Machine Translation; Hierarchical Phrase-Based SMT

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APA (6th Edition):

Almaghout, H. (2013). CCG-augmented hierarchical phrase-based statistical machine translation. (Thesis). Dublin City University. Retrieved from http://doras.dcu.ie/17450/

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):

Almaghout, Hala. “CCG-augmented hierarchical phrase-based statistical machine translation.” 2013. Thesis, Dublin City University. Accessed November 22, 2019. http://doras.dcu.ie/17450/.

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

MLA Handbook (7th Edition):

Almaghout, Hala. “CCG-augmented hierarchical phrase-based statistical machine translation.” 2013. Web. 22 Nov 2019.

Vancouver:

Almaghout H. CCG-augmented hierarchical phrase-based statistical machine translation. [Internet] [Thesis]. Dublin City University; 2013. [cited 2019 Nov 22]. Available from: http://doras.dcu.ie/17450/.

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

Council of Science Editors:

Almaghout H. CCG-augmented hierarchical phrase-based statistical machine translation. [Thesis]. Dublin City University; 2013. Available from: http://doras.dcu.ie/17450/

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


Dublin City University

5. Banerjee, Pratyush. Domain adaptation for statistical machine translation of corporate and user-generated content.

Degree: School of Computing, 2013, Dublin City University

 The growing popularity of Statistical Machine Translation (SMT) techniques in recent years has led to the development of multiple domain-specic resources and adaptation scenarios. In… (more)

Subjects/Keywords: Computational linguistics; Machine translating; Machine learning; Statistical Machine Translation; SMT

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APA (6th Edition):

Banerjee, P. (2013). Domain adaptation for statistical machine translation of corporate and user-generated content. (Thesis). Dublin City University. Retrieved from http://doras.dcu.ie/17722/

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):

Banerjee, Pratyush. “Domain adaptation for statistical machine translation of corporate and user-generated content.” 2013. Thesis, Dublin City University. Accessed November 22, 2019. http://doras.dcu.ie/17722/.

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

MLA Handbook (7th Edition):

Banerjee, Pratyush. “Domain adaptation for statistical machine translation of corporate and user-generated content.” 2013. Web. 22 Nov 2019.

Vancouver:

Banerjee P. Domain adaptation for statistical machine translation of corporate and user-generated content. [Internet] [Thesis]. Dublin City University; 2013. [cited 2019 Nov 22]. Available from: http://doras.dcu.ie/17722/.

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

Council of Science Editors:

Banerjee P. Domain adaptation for statistical machine translation of corporate and user-generated content. [Thesis]. Dublin City University; 2013. Available from: http://doras.dcu.ie/17722/

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


Hong Kong University of Science and Technology

6. 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 November 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 Nov 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 Nov 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


University of New South Wales

7. 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 (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 November 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 Nov 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 Nov 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


Rutgers University

8. McGinity, Curtis, 1987-. Optimal learning via dynamic risk.

Degree: PhD, Operations Research, 2017, Rutgers University

We consider the dilemma of taking sequential action within a nebulous and costly stochastic system. In such problems, the decision-maker sequentially takes an action from… (more)

Subjects/Keywords: Bayesian statistical decision theory; Machine learning

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APA (6th Edition):

McGinity, Curtis, 1. (2017). Optimal learning via dynamic risk. (Doctoral Dissertation). Rutgers University. Retrieved from https://rucore.libraries.rutgers.edu/rutgers-lib/53742/

Chicago Manual of Style (16th Edition):

McGinity, Curtis, 1987-. “Optimal learning via dynamic risk.” 2017. Doctoral Dissertation, Rutgers University. Accessed November 22, 2019. https://rucore.libraries.rutgers.edu/rutgers-lib/53742/.

MLA Handbook (7th Edition):

McGinity, Curtis, 1987-. “Optimal learning via dynamic risk.” 2017. Web. 22 Nov 2019.

Vancouver:

McGinity, Curtis 1. Optimal learning via dynamic risk. [Internet] [Doctoral dissertation]. Rutgers University; 2017. [cited 2019 Nov 22]. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/53742/.

Council of Science Editors:

McGinity, Curtis 1. Optimal learning via dynamic risk. [Doctoral Dissertation]. Rutgers University; 2017. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/53742/


University of Sydney

9. Silva, Mark Daniel Basco. Probabilistic monthly flood forecasting models using statistical and machine learning approaches .

Degree: 2019, University of Sydney

 Floods are considered the most damaging of natural hazards, and their frequency and damage is predicted to increase in the future. This research aims to… (more)

Subjects/Keywords: probabilistic flood forcasting; statistical machine learning

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APA (6th Edition):

Silva, M. D. B. (2019). Probabilistic monthly flood forecasting models using statistical and machine learning approaches . (Thesis). University of Sydney. Retrieved from http://hdl.handle.net/2123/20934

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):

Silva, Mark Daniel Basco. “Probabilistic monthly flood forecasting models using statistical and machine learning approaches .” 2019. Thesis, University of Sydney. Accessed November 22, 2019. http://hdl.handle.net/2123/20934.

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

MLA Handbook (7th Edition):

Silva, Mark Daniel Basco. “Probabilistic monthly flood forecasting models using statistical and machine learning approaches .” 2019. Web. 22 Nov 2019.

Vancouver:

Silva MDB. Probabilistic monthly flood forecasting models using statistical and machine learning approaches . [Internet] [Thesis]. University of Sydney; 2019. [cited 2019 Nov 22]. Available from: http://hdl.handle.net/2123/20934.

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

Council of Science Editors:

Silva MDB. Probabilistic monthly flood forecasting models using statistical and machine learning approaches . [Thesis]. University of Sydney; 2019. Available from: http://hdl.handle.net/2123/20934

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


Columbia University

10. 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 (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 November 22, 2019. https://doi.org/10.7916/D83X8CBB.

MLA Handbook (7th Edition):

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

Vancouver:

Ma Y. Flexible Sparse Learning of Feature Subspaces. [Internet] [Doctoral dissertation]. Columbia University; 2017. [cited 2019 Nov 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


Dublin City University

11. Dandapat, Sandipan. Mitigating the problems of SMT using EBMT.

Degree: Centre for Next Generation Localisation (CNGL); Dublin City University. National Centre for Language Technology (NCLT); Dublin City University. School of Computing, 2012, Dublin City University

Statistical Machine Translation (SMT) typically has difficulties with less-resourced languages even with homogeneous data. In this thesis we address the application of Example-Based Machine Translation… (more)

Subjects/Keywords: Machine translating; Computational linguistics; Machine learning; Statistical Machine Translation; SMT; Example-Based Machine Translation; EBMT

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APA (6th Edition):

Dandapat, S. (2012). Mitigating the problems of SMT using EBMT. (Thesis). Dublin City University. Retrieved from http://doras.dcu.ie/17190/

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):

Dandapat, Sandipan. “Mitigating the problems of SMT using EBMT.” 2012. Thesis, Dublin City University. Accessed November 22, 2019. http://doras.dcu.ie/17190/.

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

MLA Handbook (7th Edition):

Dandapat, Sandipan. “Mitigating the problems of SMT using EBMT.” 2012. Web. 22 Nov 2019.

Vancouver:

Dandapat S. Mitigating the problems of SMT using EBMT. [Internet] [Thesis]. Dublin City University; 2012. [cited 2019 Nov 22]. Available from: http://doras.dcu.ie/17190/.

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

Council of Science Editors:

Dandapat S. Mitigating the problems of SMT using EBMT. [Thesis]. Dublin City University; 2012. Available from: http://doras.dcu.ie/17190/

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


Northeastern University

12. 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 November 22, 2019. http://hdl.handle.net/2047/D20260369.

MLA Handbook (7th Edition):

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

Vancouver:

Shaker M. Manifold learning and unwrapping using density ridges. [Internet] [Doctoral dissertation]. Northeastern University; 2016. [cited 2019 Nov 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

13. Lesieur, Thibault. Factorisation matricielle et tensorielle par une approche issue de la physique statistique : Matricial and tensorial factorisation using tools coming from statistical physics.

Degree: Docteur es, Physique, 2017, Paris Saclay

Dans cette thèse, je présente des résultats sur la factorisation de matrice et de tenseur. Les matrices étant un objet omniprésent en mathématique, un grand… (more)

Subjects/Keywords: Physique statistique; Apprentissage machine; Informatique; Statistical physics; Machine learning; Computer science

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APA (6th Edition):

Lesieur, T. (2017). Factorisation matricielle et tensorielle par une approche issue de la physique statistique : Matricial and tensorial factorisation using tools coming from statistical physics. (Doctoral Dissertation). Paris Saclay. Retrieved from http://www.theses.fr/2017SACLS345

Chicago Manual of Style (16th Edition):

Lesieur, Thibault. “Factorisation matricielle et tensorielle par une approche issue de la physique statistique : Matricial and tensorial factorisation using tools coming from statistical physics.” 2017. Doctoral Dissertation, Paris Saclay. Accessed November 22, 2019. http://www.theses.fr/2017SACLS345.

MLA Handbook (7th Edition):

Lesieur, Thibault. “Factorisation matricielle et tensorielle par une approche issue de la physique statistique : Matricial and tensorial factorisation using tools coming from statistical physics.” 2017. Web. 22 Nov 2019.

Vancouver:

Lesieur T. Factorisation matricielle et tensorielle par une approche issue de la physique statistique : Matricial and tensorial factorisation using tools coming from statistical physics. [Internet] [Doctoral dissertation]. Paris Saclay; 2017. [cited 2019 Nov 22]. Available from: http://www.theses.fr/2017SACLS345.

Council of Science Editors:

Lesieur T. Factorisation matricielle et tensorielle par une approche issue de la physique statistique : Matricial and tensorial factorisation using tools coming from statistical physics. [Doctoral Dissertation]. Paris Saclay; 2017. Available from: http://www.theses.fr/2017SACLS345


Deakin University

14. Nguygen, Tu Dinh. Structured representation learning from complex data.

Degree: 2015, Deakin University

 This thesis advances several theoretical and practical aspects of the recently introduced restricted Boltzmann machine - a powerful probabilistic and generative framework for modelling data… (more)

Subjects/Keywords: statistical machine learning; annotated labels; restricted Boltzmann machine; structured representations

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APA (6th Edition):

Nguygen, T. D. (2015). Structured representation learning from complex data. (Thesis). Deakin University. Retrieved from http://hdl.handle.net/10536/DRO/DU:30079713

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):

Nguygen, Tu Dinh. “Structured representation learning from complex data.” 2015. Thesis, Deakin University. Accessed November 22, 2019. http://hdl.handle.net/10536/DRO/DU:30079713.

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

MLA Handbook (7th Edition):

Nguygen, Tu Dinh. “Structured representation learning from complex data.” 2015. Web. 22 Nov 2019.

Vancouver:

Nguygen TD. Structured representation learning from complex data. [Internet] [Thesis]. Deakin University; 2015. [cited 2019 Nov 22]. Available from: http://hdl.handle.net/10536/DRO/DU:30079713.

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

Council of Science Editors:

Nguygen TD. Structured representation learning from complex data. [Thesis]. Deakin University; 2015. Available from: http://hdl.handle.net/10536/DRO/DU:30079713

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


McMaster University

15. 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 November 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 Nov 2019.

Vancouver:

Blostein M. An Efficient Implementation of a Robust Clustering Algorithm. [Internet] [Masters thesis]. McMaster University; 2016. [cited 2019 Nov 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


Columbia University

16. Liu, Ying. Statistical Learning Methods for Personalized Medical Decision Making.

Degree: 2016, Columbia University

 The theme of my dissertation is on merging statistical modeling with medical domain knowledge and machine learning algorithms to assist in making personalized medical decisions.… (more)

Subjects/Keywords: Machine learning; Biometry; Therapeutics; Multiple imputation (Statistics); Medical statistics; Machine learning – Statistical methods

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

APA (6th Edition):

Liu, Y. (2016). Statistical Learning Methods for Personalized Medical Decision Making. (Doctoral Dissertation). Columbia University. Retrieved from https://doi.org/10.7916/D8HH6K22

Chicago Manual of Style (16th Edition):

Liu, Ying. “Statistical Learning Methods for Personalized Medical Decision Making.” 2016. Doctoral Dissertation, Columbia University. Accessed November 22, 2019. https://doi.org/10.7916/D8HH6K22.

MLA Handbook (7th Edition):

Liu, Ying. “Statistical Learning Methods for Personalized Medical Decision Making.” 2016. Web. 22 Nov 2019.

Vancouver:

Liu Y. Statistical Learning Methods for Personalized Medical Decision Making. [Internet] [Doctoral dissertation]. Columbia University; 2016. [cited 2019 Nov 22]. Available from: https://doi.org/10.7916/D8HH6K22.

Council of Science Editors:

Liu Y. Statistical Learning Methods for Personalized Medical Decision Making. [Doctoral Dissertation]. Columbia University; 2016. Available from: https://doi.org/10.7916/D8HH6K22


Purdue University

17. Zilqurnain Naqvi, Syed Abbas Zilqurnain Naqvi. Efficient Sparse Bayesian Learning using Spike-and-Slab Priors.

Degree: PhD, Computer Science, 2016, Purdue University

 In the context of statistical machine learning, sparse learning is a procedure that seeks a reconciliation between two competing aspects of a statistical model: good… (more)

Subjects/Keywords: Classification; Regression; Sparse Bayesian Learning; Statistical Machine Learning; Supervised Learning; Variable Selection

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

APA (6th Edition):

Zilqurnain Naqvi, S. A. Z. N. (2016). Efficient Sparse Bayesian Learning using Spike-and-Slab Priors. (Doctoral Dissertation). Purdue University. Retrieved from https://docs.lib.purdue.edu/open_access_dissertations/1402

Chicago Manual of Style (16th Edition):

Zilqurnain Naqvi, Syed Abbas Zilqurnain Naqvi. “Efficient Sparse Bayesian Learning using Spike-and-Slab Priors.” 2016. Doctoral Dissertation, Purdue University. Accessed November 22, 2019. https://docs.lib.purdue.edu/open_access_dissertations/1402.

MLA Handbook (7th Edition):

Zilqurnain Naqvi, Syed Abbas Zilqurnain Naqvi. “Efficient Sparse Bayesian Learning using Spike-and-Slab Priors.” 2016. Web. 22 Nov 2019.

Vancouver:

Zilqurnain Naqvi SAZN. Efficient Sparse Bayesian Learning using Spike-and-Slab Priors. [Internet] [Doctoral dissertation]. Purdue University; 2016. [cited 2019 Nov 22]. Available from: https://docs.lib.purdue.edu/open_access_dissertations/1402.

Council of Science Editors:

Zilqurnain Naqvi SAZN. Efficient Sparse Bayesian Learning using Spike-and-Slab Priors. [Doctoral Dissertation]. Purdue University; 2016. Available from: https://docs.lib.purdue.edu/open_access_dissertations/1402


Cornell University

18. Foster, Dylan James. Adaptive Learning: Algorithms and Complexity .

Degree: 2019, Cornell University

 Recent empirical success in machine learning has led to major breakthroughs in application domains including computer vision, robotics, and natural language processing. There is a… (more)

Subjects/Keywords: Statistics; adaptivity; bandits; statistical learning; Optimization; Computer science; Online learning; machine learning

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

APA (6th Edition):

Foster, D. J. (2019). Adaptive Learning: Algorithms and Complexity . (Thesis). Cornell University. Retrieved from http://hdl.handle.net/1813/67219

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):

Foster, Dylan James. “Adaptive Learning: Algorithms and Complexity .” 2019. Thesis, Cornell University. Accessed November 22, 2019. http://hdl.handle.net/1813/67219.

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

MLA Handbook (7th Edition):

Foster, Dylan James. “Adaptive Learning: Algorithms and Complexity .” 2019. Web. 22 Nov 2019.

Vancouver:

Foster DJ. Adaptive Learning: Algorithms and Complexity . [Internet] [Thesis]. Cornell University; 2019. [cited 2019 Nov 22]. Available from: http://hdl.handle.net/1813/67219.

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

Council of Science Editors:

Foster DJ. Adaptive Learning: Algorithms and Complexity . [Thesis]. Cornell University; 2019. Available from: http://hdl.handle.net/1813/67219

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


University of Notre Dame

19. Raymond Kenney Walters. Evaluation of a Two-Stage Statistical Learning Design for Genome-Wide Studies</h1>.

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

  Twin and family studies show that many common traits and disorders are highly heritable, but genome-wide association studies (GWAS) have been largely unable to… (more)

Subjects/Keywords: data mining; machine learning; statistical genetics; regression trees; behavioral genetics; variable importance; statistical power

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

APA (6th Edition):

Walters, R. K. (2013). Evaluation of a Two-Stage Statistical Learning Design for Genome-Wide Studies</h1>. (Masters Thesis). University of Notre Dame. Retrieved from https://curate.nd.edu/show/p2676t07f3d

Chicago Manual of Style (16th Edition):

Walters, Raymond Kenney. “Evaluation of a Two-Stage Statistical Learning Design for Genome-Wide Studies</h1>.” 2013. Masters Thesis, University of Notre Dame. Accessed November 22, 2019. https://curate.nd.edu/show/p2676t07f3d.

MLA Handbook (7th Edition):

Walters, Raymond Kenney. “Evaluation of a Two-Stage Statistical Learning Design for Genome-Wide Studies</h1>.” 2013. Web. 22 Nov 2019.

Vancouver:

Walters RK. Evaluation of a Two-Stage Statistical Learning Design for Genome-Wide Studies</h1>. [Internet] [Masters thesis]. University of Notre Dame; 2013. [cited 2019 Nov 22]. Available from: https://curate.nd.edu/show/p2676t07f3d.

Council of Science Editors:

Walters RK. Evaluation of a Two-Stage Statistical Learning Design for Genome-Wide Studies</h1>. [Masters Thesis]. University of Notre Dame; 2013. Available from: https://curate.nd.edu/show/p2676t07f3d


Dublin City University

20. Penkale, Sergio. Incorporating translation quality-oriented features into log-linear models of machine translation.

Degree: Centre for Next Generation Localisation (CNGL); Dublin City University. School of Computing, 2011, Dublin City University

 The current state-of-the-art approach to Machine Translation (MT) has limitations which could be alleviated by the use of syntax-based models. Although the benefits of syntax… (more)

Subjects/Keywords: Computational linguistics; Machine translating; Machine learning; Data Oriented Translation; DOT; PhraseBased Statistical Translation; PB-SMT

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

APA (6th Edition):

Penkale, S. (2011). Incorporating translation quality-oriented features into log-linear models of machine translation. (Thesis). Dublin City University. Retrieved from http://doras.dcu.ie/16464/

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):

Penkale, Sergio. “Incorporating translation quality-oriented features into log-linear models of machine translation.” 2011. Thesis, Dublin City University. Accessed November 22, 2019. http://doras.dcu.ie/16464/.

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

MLA Handbook (7th Edition):

Penkale, Sergio. “Incorporating translation quality-oriented features into log-linear models of machine translation.” 2011. Web. 22 Nov 2019.

Vancouver:

Penkale S. Incorporating translation quality-oriented features into log-linear models of machine translation. [Internet] [Thesis]. Dublin City University; 2011. [cited 2019 Nov 22]. Available from: http://doras.dcu.ie/16464/.

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

Council of Science Editors:

Penkale S. Incorporating translation quality-oriented features into log-linear models of machine translation. [Thesis]. Dublin City University; 2011. Available from: http://doras.dcu.ie/16464/

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


University of Utah

21. Panthail, Jai kanth. The impact of black carbon deposition on snowpack and streamflow in the wasatch mountains in utah: a study using modis albedo data, statistical modeling and machine learning.

Degree: MS, Civil & Environmental Engineering, 2015, University of Utah

 Salt Lake City, located at the base of the Wasatch mountain range in Utah, receives a majority of its potable water from a system of… (more)

Subjects/Keywords: hydrology; machine learning; MODIS; snow; statistical; water resources

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APA (6th Edition):

Panthail, J. k. (2015). The impact of black carbon deposition on snowpack and streamflow in the wasatch mountains in utah: a study using modis albedo data, statistical modeling and machine learning. (Masters Thesis). University of Utah. Retrieved from http://content.lib.utah.edu/cdm/singleitem/collection/etd3/id/3823/rec/2547

Chicago Manual of Style (16th Edition):

Panthail, Jai kanth. “The impact of black carbon deposition on snowpack and streamflow in the wasatch mountains in utah: a study using modis albedo data, statistical modeling and machine learning.” 2015. Masters Thesis, University of Utah. Accessed November 22, 2019. http://content.lib.utah.edu/cdm/singleitem/collection/etd3/id/3823/rec/2547.

MLA Handbook (7th Edition):

Panthail, Jai kanth. “The impact of black carbon deposition on snowpack and streamflow in the wasatch mountains in utah: a study using modis albedo data, statistical modeling and machine learning.” 2015. Web. 22 Nov 2019.

Vancouver:

Panthail Jk. The impact of black carbon deposition on snowpack and streamflow in the wasatch mountains in utah: a study using modis albedo data, statistical modeling and machine learning. [Internet] [Masters thesis]. University of Utah; 2015. [cited 2019 Nov 22]. Available from: http://content.lib.utah.edu/cdm/singleitem/collection/etd3/id/3823/rec/2547.

Council of Science Editors:

Panthail Jk. The impact of black carbon deposition on snowpack and streamflow in the wasatch mountains in utah: a study using modis albedo data, statistical modeling and machine learning. [Masters Thesis]. University of Utah; 2015. Available from: http://content.lib.utah.edu/cdm/singleitem/collection/etd3/id/3823/rec/2547


University of Illinois – Chicago

22. Nazarian, Ebrahim. Machine Learning, Probabilistic and Mathematical Models for Damage Recognition in Structural Systems.

Degree: 2017, University of Illinois – Chicago

 An increasing percentage of building and bridge structures across United States are exceeding their design life. Ensuring the structural integrity of such structures demands health… (more)

Subjects/Keywords: Machine learning; Mathematical Models; Statistical Models; Structural Health Monitoring

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

APA (6th Edition):

Nazarian, E. (2017). Machine Learning, Probabilistic and Mathematical Models for Damage Recognition in Structural Systems. (Thesis). University of Illinois – Chicago. Retrieved from http://hdl.handle.net/10027/21922

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):

Nazarian, Ebrahim. “Machine Learning, Probabilistic and Mathematical Models for Damage Recognition in Structural Systems.” 2017. Thesis, University of Illinois – Chicago. Accessed November 22, 2019. http://hdl.handle.net/10027/21922.

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

MLA Handbook (7th Edition):

Nazarian, Ebrahim. “Machine Learning, Probabilistic and Mathematical Models for Damage Recognition in Structural Systems.” 2017. Web. 22 Nov 2019.

Vancouver:

Nazarian E. Machine Learning, Probabilistic and Mathematical Models for Damage Recognition in Structural Systems. [Internet] [Thesis]. University of Illinois – Chicago; 2017. [cited 2019 Nov 22]. Available from: http://hdl.handle.net/10027/21922.

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

Council of Science Editors:

Nazarian E. Machine Learning, Probabilistic and Mathematical Models for Damage Recognition in Structural Systems. [Thesis]. University of Illinois – Chicago; 2017. Available from: http://hdl.handle.net/10027/21922

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


Anna University

23. Sakthivel S. An improved multiple weighted facial attribute set based face recognition system using statistical and machine learning techniques;.

Degree: 2013, Anna University

In recent years, research on face recognition has attracted more and more attention from both academia and industry. Face Recognition has become an important concept… (more)

Subjects/Keywords: Statistical techniques; machine learning techniques; Multiple Weighted Facial Attribute Sets

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APA (6th Edition):

S, S. (2013). An improved multiple weighted facial attribute set based face recognition system using statistical and machine learning techniques;. (Thesis). Anna University. Retrieved from http://shodhganga.inflibnet.ac.in/handle/10603/13445

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):

S, Sakthivel. “An improved multiple weighted facial attribute set based face recognition system using statistical and machine learning techniques;.” 2013. Thesis, Anna University. Accessed November 22, 2019. http://shodhganga.inflibnet.ac.in/handle/10603/13445.

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

MLA Handbook (7th Edition):

S, Sakthivel. “An improved multiple weighted facial attribute set based face recognition system using statistical and machine learning techniques;.” 2013. Web. 22 Nov 2019.

Vancouver:

S S. An improved multiple weighted facial attribute set based face recognition system using statistical and machine learning techniques;. [Internet] [Thesis]. Anna University; 2013. [cited 2019 Nov 22]. Available from: http://shodhganga.inflibnet.ac.in/handle/10603/13445.

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

Council of Science Editors:

S S. An improved multiple weighted facial attribute set based face recognition system using statistical and machine learning techniques;. [Thesis]. Anna University; 2013. Available from: http://shodhganga.inflibnet.ac.in/handle/10603/13445

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


Anna University

24. Ramesh K. Application of data mining techniques for Novel ensemble forecast of lead seven Days minimum and maximum surface air Temperature in Chennai india;.

Degree: Application of data mining techniques for Novel ensemble forecast of lead seven Days minimum and maximum surface air Temperature in Chennai india, 2015, Anna University

Evolution in data storage and large databases has generated an newlineimperative need for new techniques and tools for data analysis and knowledge newlinediscovery Statistical computational… (more)

Subjects/Keywords: Predictive data mining; Statistical computational and machine learning tools

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APA (6th Edition):

K, R. (2015). Application of data mining techniques for Novel ensemble forecast of lead seven Days minimum and maximum surface air Temperature in Chennai india;. (Thesis). Anna University. Retrieved from http://shodhganga.inflibnet.ac.in/handle/10603/38555

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):

K, Ramesh. “Application of data mining techniques for Novel ensemble forecast of lead seven Days minimum and maximum surface air Temperature in Chennai india;.” 2015. Thesis, Anna University. Accessed November 22, 2019. http://shodhganga.inflibnet.ac.in/handle/10603/38555.

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

MLA Handbook (7th Edition):

K, Ramesh. “Application of data mining techniques for Novel ensemble forecast of lead seven Days minimum and maximum surface air Temperature in Chennai india;.” 2015. Web. 22 Nov 2019.

Vancouver:

K R. Application of data mining techniques for Novel ensemble forecast of lead seven Days minimum and maximum surface air Temperature in Chennai india;. [Internet] [Thesis]. Anna University; 2015. [cited 2019 Nov 22]. Available from: http://shodhganga.inflibnet.ac.in/handle/10603/38555.

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

Council of Science Editors:

K R. Application of data mining techniques for Novel ensemble forecast of lead seven Days minimum and maximum surface air Temperature in Chennai india;. [Thesis]. Anna University; 2015. Available from: http://shodhganga.inflibnet.ac.in/handle/10603/38555

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


Queensland University of Technology

25. Wang, Hao Xing. Developing and testing readability measurements for second language learners.

Degree: 2016, Queensland University of Technology

 This research constructed a readability measurement for French speakers who view English as a second language. It identified the true cognates, which are the similar… (more)

Subjects/Keywords: Readability Assessment; Cognate Identification; Multilingual lexical; Machine Learning; Statistical Language Model

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

APA (6th Edition):

Wang, H. X. (2016). Developing and testing readability measurements for second language learners. (Thesis). Queensland University of Technology. Retrieved from http://eprints.qut.edu.au/95111/

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 Xing. “Developing and testing readability measurements for second language learners.” 2016. Thesis, Queensland University of Technology. Accessed November 22, 2019. http://eprints.qut.edu.au/95111/.

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 Xing. “Developing and testing readability measurements for second language learners.” 2016. Web. 22 Nov 2019.

Vancouver:

Wang HX. Developing and testing readability measurements for second language learners. [Internet] [Thesis]. Queensland University of Technology; 2016. [cited 2019 Nov 22]. Available from: http://eprints.qut.edu.au/95111/.

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

Council of Science Editors:

Wang HX. Developing and testing readability measurements for second language learners. [Thesis]. Queensland University of Technology; 2016. Available from: http://eprints.qut.edu.au/95111/

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


Hong Kong University of Science and Technology

26. Chen, Mingxing. Efficient learning of hierarchical naive bayes models.

Degree: 2010, Hong Kong University of Science and Technology

 Hierarchical naive Bayes (HNB) model [21] is useful in latent variable discovery and classification. It introduces latent variables to a naive Bayes (NB) model to… (more)

Subjects/Keywords: Bayesian statistical decision theory; Machine learning  – Mathematical models

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APA (6th Edition):

Chen, M. (2010). Efficient learning of hierarchical naive bayes models. (Thesis). Hong Kong University of Science and Technology. Retrieved from https://doi.org/10.14711/thesis-b1115147 ; http://repository.ust.hk/ir/bitstream/1783.1-6948/1/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):

Chen, Mingxing. “Efficient learning of hierarchical naive bayes models.” 2010. Thesis, Hong Kong University of Science and Technology. Accessed November 22, 2019. https://doi.org/10.14711/thesis-b1115147 ; http://repository.ust.hk/ir/bitstream/1783.1-6948/1/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):

Chen, Mingxing. “Efficient learning of hierarchical naive bayes models.” 2010. Web. 22 Nov 2019.

Vancouver:

Chen M. Efficient learning of hierarchical naive bayes models. [Internet] [Thesis]. Hong Kong University of Science and Technology; 2010. [cited 2019 Nov 22]. Available from: https://doi.org/10.14711/thesis-b1115147 ; http://repository.ust.hk/ir/bitstream/1783.1-6948/1/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:

Chen M. Efficient learning of hierarchical naive bayes models. [Thesis]. Hong Kong University of Science and Technology; 2010. Available from: https://doi.org/10.14711/thesis-b1115147 ; http://repository.ust.hk/ir/bitstream/1783.1-6948/1/th_redirect.html

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


Hong Kong University of Science and Technology

27. Li, Wu-Jun. Latent factor models for statistical relational learning.

Degree: 2010, Hong Kong University of Science and Technology

 To simplify modeling procedures, traditional statistical machine learning methods always assume that the instances are independent and identically distributed (i.i.d.). However, it is not uncommon… (more)

Subjects/Keywords: Relational databases; Machine learning  – Statistical methods; Latent variables

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APA (6th Edition):

Li, W. (2010). Latent factor models for statistical relational learning. (Thesis). Hong Kong University of Science and Technology. Retrieved from https://doi.org/10.14711/thesis-b1115031 ; http://repository.ust.hk/ir/bitstream/1783.1-6928/1/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):

Li, Wu-Jun. “Latent factor models for statistical relational learning.” 2010. Thesis, Hong Kong University of Science and Technology. Accessed November 22, 2019. https://doi.org/10.14711/thesis-b1115031 ; http://repository.ust.hk/ir/bitstream/1783.1-6928/1/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):

Li, Wu-Jun. “Latent factor models for statistical relational learning.” 2010. Web. 22 Nov 2019.

Vancouver:

Li W. Latent factor models for statistical relational learning. [Internet] [Thesis]. Hong Kong University of Science and Technology; 2010. [cited 2019 Nov 22]. Available from: https://doi.org/10.14711/thesis-b1115031 ; http://repository.ust.hk/ir/bitstream/1783.1-6928/1/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:

Li W. Latent factor models for statistical relational learning. [Thesis]. Hong Kong University of Science and Technology; 2010. Available from: https://doi.org/10.14711/thesis-b1115031 ; http://repository.ust.hk/ir/bitstream/1783.1-6928/1/th_redirect.html

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


Hong Kong University of Science and Technology

28. Zheng, Shuai. Stochastic alternating direction method of multipliers.

Degree: 2015, Hong Kong University of Science and Technology

 The alternating direction method of multipliers (ADMM) is an efficient optimization solver for a wide variety of machine learning models. Recently, stochastic ADMM has been… (more)

Subjects/Keywords: Machine learning; Mathematical models; Statistical methods; Multipliers (Mathematical analysis); Stochastic geometry

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APA (6th Edition):

Zheng, S. (2015). Stochastic alternating direction method of multipliers. (Thesis). Hong Kong University of Science and Technology. Retrieved from https://doi.org/10.14711/thesis-b1514777 ; http://repository.ust.hk/ir/bitstream/1783.1-78847/1/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):

Zheng, Shuai. “Stochastic alternating direction method of multipliers.” 2015. Thesis, Hong Kong University of Science and Technology. Accessed November 22, 2019. https://doi.org/10.14711/thesis-b1514777 ; http://repository.ust.hk/ir/bitstream/1783.1-78847/1/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):

Zheng, Shuai. “Stochastic alternating direction method of multipliers.” 2015. Web. 22 Nov 2019.

Vancouver:

Zheng S. Stochastic alternating direction method of multipliers. [Internet] [Thesis]. Hong Kong University of Science and Technology; 2015. [cited 2019 Nov 22]. Available from: https://doi.org/10.14711/thesis-b1514777 ; http://repository.ust.hk/ir/bitstream/1783.1-78847/1/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:

Zheng S. Stochastic alternating direction method of multipliers. [Thesis]. Hong Kong University of Science and Technology; 2015. Available from: https://doi.org/10.14711/thesis-b1514777 ; http://repository.ust.hk/ir/bitstream/1783.1-78847/1/th_redirect.html

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


University of Southern California

29. Lammert, Adam C. Structure and function in speech production.

Degree: PhD, Computer Science, 2014, University of Southern California

 The mechanisms underlying speech production are some of the most crucial that humans posses, because the ability to produce and perceive speech forms much of… (more)

Subjects/Keywords: speech production; motor control; physical acoustics; statistical machine learning

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APA (6th Edition):

Lammert, A. C. (2014). Structure and function in speech production. (Doctoral Dissertation). University of Southern California. Retrieved from http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/422765/rec/6123

Chicago Manual of Style (16th Edition):

Lammert, Adam C. “Structure and function in speech production.” 2014. Doctoral Dissertation, University of Southern California. Accessed November 22, 2019. http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/422765/rec/6123.

MLA Handbook (7th Edition):

Lammert, Adam C. “Structure and function in speech production.” 2014. Web. 22 Nov 2019.

Vancouver:

Lammert AC. Structure and function in speech production. [Internet] [Doctoral dissertation]. University of Southern California; 2014. [cited 2019 Nov 22]. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/422765/rec/6123.

Council of Science Editors:

Lammert AC. Structure and function in speech production. [Doctoral Dissertation]. University of Southern California; 2014. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/422765/rec/6123


University of Florida

30. Brahma, Pratik P. Reliable Subspace Representation for Better Learning and Understanding of Data.

Degree: PhD, Electrical and Computer Engineering, 2016, University of Florida

Subjects/Keywords: deep; learning; machine; statistical; subspace

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

APA (6th Edition):

Brahma, P. P. (2016). Reliable Subspace Representation for Better Learning and Understanding of Data. (Doctoral Dissertation). University of Florida. Retrieved from http://ufdc.ufl.edu/UFE0050287

Chicago Manual of Style (16th Edition):

Brahma, Pratik P. “Reliable Subspace Representation for Better Learning and Understanding of Data.” 2016. Doctoral Dissertation, University of Florida. Accessed November 22, 2019. http://ufdc.ufl.edu/UFE0050287.

MLA Handbook (7th Edition):

Brahma, Pratik P. “Reliable Subspace Representation for Better Learning and Understanding of Data.” 2016. Web. 22 Nov 2019.

Vancouver:

Brahma PP. Reliable Subspace Representation for Better Learning and Understanding of Data. [Internet] [Doctoral dissertation]. University of Florida; 2016. [cited 2019 Nov 22]. Available from: http://ufdc.ufl.edu/UFE0050287.

Council of Science Editors:

Brahma PP. Reliable Subspace Representation for Better Learning and Understanding of Data. [Doctoral Dissertation]. University of Florida; 2016. Available from: http://ufdc.ufl.edu/UFE0050287

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