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You searched for +publisher:"University of Manchester" +contributor:("Brown, Gavin"). Showing records 1 – 14 of 14 total matches.

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University of Manchester

1. Zanda, Manuela. A Probabilistic Perspective on Ensemble Diversity.

Degree: 2010, University of Manchester

 We study diversity in classifier ensembles from a broader perspectivethan the 0/1 loss function, the main reason being that thebias-variance decomposition of the 0/1 loss… (more)

Subjects/Keywords: Ensemble Learning; classifier diversity; Machine Learning; Information Theory

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APA (6th Edition):

Zanda, M. (2010). A Probabilistic Perspective on Ensemble Diversity. (Doctoral Dissertation). University of Manchester. Retrieved from http://www.manchester.ac.uk/escholar/uk-ac-man-scw:94566

Chicago Manual of Style (16th Edition):

Zanda, Manuela. “A Probabilistic Perspective on Ensemble Diversity.” 2010. Doctoral Dissertation, University of Manchester. Accessed June 18, 2019. http://www.manchester.ac.uk/escholar/uk-ac-man-scw:94566.

MLA Handbook (7th Edition):

Zanda, Manuela. “A Probabilistic Perspective on Ensemble Diversity.” 2010. Web. 18 Jun 2019.

Vancouver:

Zanda M. A Probabilistic Perspective on Ensemble Diversity. [Internet] [Doctoral dissertation]. University of Manchester; 2010. [cited 2019 Jun 18]. Available from: http://www.manchester.ac.uk/escholar/uk-ac-man-scw:94566.

Council of Science Editors:

Zanda M. A Probabilistic Perspective on Ensemble Diversity. [Doctoral Dissertation]. University of Manchester; 2010. Available from: http://www.manchester.ac.uk/escholar/uk-ac-man-scw:94566


University of Manchester

2. Sazonau, Viachaslau. General Terminology Induction in Description Logics.

Degree: 2017, University of Manchester

 In computer science, an ontology is a machine-processable representation of knowledge about some domain. Ontologies are encoded in ontology languages, such as the Web Ontology… (more)

Subjects/Keywords: Ontology; Web Ontology Language; Description Logics; Learning; Reasoning; Mining; General Terminology Induction; DL-Miner; Ontology Learning; Data Mining; Axiom; OWL; Machine Learning; Artificial Intelligence

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APA (6th Edition):

Sazonau, V. (2017). General Terminology Induction in Description Logics. (Doctoral Dissertation). University of Manchester. Retrieved from http://www.manchester.ac.uk/escholar/uk-ac-man-scw:309721

Chicago Manual of Style (16th Edition):

Sazonau, Viachaslau. “General Terminology Induction in Description Logics.” 2017. Doctoral Dissertation, University of Manchester. Accessed June 18, 2019. http://www.manchester.ac.uk/escholar/uk-ac-man-scw:309721.

MLA Handbook (7th Edition):

Sazonau, Viachaslau. “General Terminology Induction in Description Logics.” 2017. Web. 18 Jun 2019.

Vancouver:

Sazonau V. General Terminology Induction in Description Logics. [Internet] [Doctoral dissertation]. University of Manchester; 2017. [cited 2019 Jun 18]. Available from: http://www.manchester.ac.uk/escholar/uk-ac-man-scw:309721.

Council of Science Editors:

Sazonau V. General Terminology Induction in Description Logics. [Doctoral Dissertation]. University of Manchester; 2017. Available from: http://www.manchester.ac.uk/escholar/uk-ac-man-scw:309721


University of Manchester

3. Filannino, Michele. Data-driven Temporal Information Extraction with Applications in General and Clinical Domains.

Degree: 2016, University of Manchester

The automatic extraction of temporal information from written texts is pivotal for many Natural Language Processing applications such as question answering, text summarisation and information… (more)

Subjects/Keywords: text mining; machine learning; temporal information extraction

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APA (6th Edition):

Filannino, M. (2016). Data-driven Temporal Information Extraction with Applications in General and Clinical Domains. (Doctoral Dissertation). University of Manchester. Retrieved from http://www.manchester.ac.uk/escholar/uk-ac-man-scw:296972

Chicago Manual of Style (16th Edition):

Filannino, Michele. “Data-driven Temporal Information Extraction with Applications in General and Clinical Domains.” 2016. Doctoral Dissertation, University of Manchester. Accessed June 18, 2019. http://www.manchester.ac.uk/escholar/uk-ac-man-scw:296972.

MLA Handbook (7th Edition):

Filannino, Michele. “Data-driven Temporal Information Extraction with Applications in General and Clinical Domains.” 2016. Web. 18 Jun 2019.

Vancouver:

Filannino M. Data-driven Temporal Information Extraction with Applications in General and Clinical Domains. [Internet] [Doctoral dissertation]. University of Manchester; 2016. [cited 2019 Jun 18]. Available from: http://www.manchester.ac.uk/escholar/uk-ac-man-scw:296972.

Council of Science Editors:

Filannino M. Data-driven Temporal Information Extraction with Applications in General and Clinical Domains. [Doctoral Dissertation]. University of Manchester; 2016. Available from: http://www.manchester.ac.uk/escholar/uk-ac-man-scw:296972


University of Manchester

4. Nogueira, Sarah. Quantifying the Stability of Feature Selection.

Degree: 2018, University of Manchester

 Feature Selection is central to modern data science, from exploratory data analysis to predictive model-building. The "stability"of a feature selection algorithm refers to the robustness… (more)

Subjects/Keywords: Stability; Feature Selection; Variable Selection

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APA (6th Edition):

Nogueira, S. (2018). Quantifying the Stability of Feature Selection. (Doctoral Dissertation). University of Manchester. Retrieved from http://www.manchester.ac.uk/escholar/uk-ac-man-scw:313287

Chicago Manual of Style (16th Edition):

Nogueira, Sarah. “Quantifying the Stability of Feature Selection.” 2018. Doctoral Dissertation, University of Manchester. Accessed June 18, 2019. http://www.manchester.ac.uk/escholar/uk-ac-man-scw:313287.

MLA Handbook (7th Edition):

Nogueira, Sarah. “Quantifying the Stability of Feature Selection.” 2018. Web. 18 Jun 2019.

Vancouver:

Nogueira S. Quantifying the Stability of Feature Selection. [Internet] [Doctoral dissertation]. University of Manchester; 2018. [cited 2019 Jun 18]. Available from: http://www.manchester.ac.uk/escholar/uk-ac-man-scw:313287.

Council of Science Editors:

Nogueira S. Quantifying the Stability of Feature Selection. [Doctoral Dissertation]. University of Manchester; 2018. Available from: http://www.manchester.ac.uk/escholar/uk-ac-man-scw:313287


University of Manchester

5. Nikolaou, Nikolaos. Cost-sensitive boosting: A unified approach.

Degree: 2016, University of Manchester

 In this thesis we provide a unifying framework for two decades of work in an area of Machine Learning known as cost-sensitive Boosting algorithms. This… (more)

Subjects/Keywords: Boosting; Adaboost; cost-sensitive; imbalanced classes; risk minimization; classifier calibration; functional gradient descent; decision theory; margin theory; product of experts; ensemble learning; probability estimation

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APA (6th Edition):

Nikolaou, N. (2016). Cost-sensitive boosting: A unified approach. (Doctoral Dissertation). University of Manchester. Retrieved from http://www.manchester.ac.uk/escholar/uk-ac-man-scw:306158

Chicago Manual of Style (16th Edition):

Nikolaou, Nikolaos. “Cost-sensitive boosting: A unified approach.” 2016. Doctoral Dissertation, University of Manchester. Accessed June 18, 2019. http://www.manchester.ac.uk/escholar/uk-ac-man-scw:306158.

MLA Handbook (7th Edition):

Nikolaou, Nikolaos. “Cost-sensitive boosting: A unified approach.” 2016. Web. 18 Jun 2019.

Vancouver:

Nikolaou N. Cost-sensitive boosting: A unified approach. [Internet] [Doctoral dissertation]. University of Manchester; 2016. [cited 2019 Jun 18]. Available from: http://www.manchester.ac.uk/escholar/uk-ac-man-scw:306158.

Council of Science Editors:

Nikolaou N. Cost-sensitive boosting: A unified approach. [Doctoral Dissertation]. University of Manchester; 2016. Available from: http://www.manchester.ac.uk/escholar/uk-ac-man-scw:306158


University of Manchester

6. Sechidis, Konstantinos. Hypothesis Testing and Feature Selection in Semi-Supervised Data.

Degree: 2015, University of Manchester

 A characteristic of most real world problems is that collecting unlabelled examples is easier and cheaper than collecting labelled ones. As a result, learning from… (more)

Subjects/Keywords: Machine Learning; Information Theory; Feature Selection; Hypothesis Testing; Semi Supervised; Positive Unlabelled; Mutual Information

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APA (6th Edition):

Sechidis, K. (2015). Hypothesis Testing and Feature Selection in Semi-Supervised Data. (Doctoral Dissertation). University of Manchester. Retrieved from http://www.manchester.ac.uk/escholar/uk-ac-man-scw:277415

Chicago Manual of Style (16th Edition):

Sechidis, Konstantinos. “Hypothesis Testing and Feature Selection in Semi-Supervised Data.” 2015. Doctoral Dissertation, University of Manchester. Accessed June 18, 2019. http://www.manchester.ac.uk/escholar/uk-ac-man-scw:277415.

MLA Handbook (7th Edition):

Sechidis, Konstantinos. “Hypothesis Testing and Feature Selection in Semi-Supervised Data.” 2015. Web. 18 Jun 2019.

Vancouver:

Sechidis K. Hypothesis Testing and Feature Selection in Semi-Supervised Data. [Internet] [Doctoral dissertation]. University of Manchester; 2015. [cited 2019 Jun 18]. Available from: http://www.manchester.ac.uk/escholar/uk-ac-man-scw:277415.

Council of Science Editors:

Sechidis K. Hypothesis Testing and Feature Selection in Semi-Supervised Data. [Doctoral Dissertation]. University of Manchester; 2015. Available from: http://www.manchester.ac.uk/escholar/uk-ac-man-scw:277415


University of Manchester

7. Turner, Emily. Predictive Variable Selection for Subgroup Identification.

Degree: 2017, University of Manchester

 The problem of exploratory subgroup identification can be broken down into three steps. The first step is to identify predictive features, the second is to… (more)

Subjects/Keywords: Subgroup identification; Interaction detection; Recursive partitioning

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APA (6th Edition):

Turner, E. (2017). Predictive Variable Selection for Subgroup Identification. (Doctoral Dissertation). University of Manchester. Retrieved from http://www.manchester.ac.uk/escholar/uk-ac-man-scw:312697

Chicago Manual of Style (16th Edition):

Turner, Emily. “Predictive Variable Selection for Subgroup Identification.” 2017. Doctoral Dissertation, University of Manchester. Accessed June 18, 2019. http://www.manchester.ac.uk/escholar/uk-ac-man-scw:312697.

MLA Handbook (7th Edition):

Turner, Emily. “Predictive Variable Selection for Subgroup Identification.” 2017. Web. 18 Jun 2019.

Vancouver:

Turner E. Predictive Variable Selection for Subgroup Identification. [Internet] [Doctoral dissertation]. University of Manchester; 2017. [cited 2019 Jun 18]. Available from: http://www.manchester.ac.uk/escholar/uk-ac-man-scw:312697.

Council of Science Editors:

Turner E. Predictive Variable Selection for Subgroup Identification. [Doctoral Dissertation]. University of Manchester; 2017. Available from: http://www.manchester.ac.uk/escholar/uk-ac-man-scw:312697


University of Manchester

8. Fairclough, Michael Edward. PET Radiochemistry for the Investigation of the biology of pain and inflammation.

Degree: 2015, University of Manchester

 Positron emission tomography (PET) is an important and powerful nuclear imaging modality and is essential in a range of medical fields. A suitable radiotracer must… (more)

Subjects/Keywords: PET; Radiochemistry; Carbon-11; Fluorine-18; Zirconium-89; Inflammation Imaging; Opioids

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APA (6th Edition):

Fairclough, M. E. (2015). PET Radiochemistry for the Investigation of the biology of pain and inflammation. (Doctoral Dissertation). University of Manchester. Retrieved from http://www.manchester.ac.uk/escholar/uk-ac-man-scw:271063

Chicago Manual of Style (16th Edition):

Fairclough, Michael Edward. “PET Radiochemistry for the Investigation of the biology of pain and inflammation.” 2015. Doctoral Dissertation, University of Manchester. Accessed June 18, 2019. http://www.manchester.ac.uk/escholar/uk-ac-man-scw:271063.

MLA Handbook (7th Edition):

Fairclough, Michael Edward. “PET Radiochemistry for the Investigation of the biology of pain and inflammation.” 2015. Web. 18 Jun 2019.

Vancouver:

Fairclough ME. PET Radiochemistry for the Investigation of the biology of pain and inflammation. [Internet] [Doctoral dissertation]. University of Manchester; 2015. [cited 2019 Jun 18]. Available from: http://www.manchester.ac.uk/escholar/uk-ac-man-scw:271063.

Council of Science Editors:

Fairclough ME. PET Radiochemistry for the Investigation of the biology of pain and inflammation. [Doctoral Dissertation]. University of Manchester; 2015. Available from: http://www.manchester.ac.uk/escholar/uk-ac-man-scw:271063

9. Stapenhurst, Richard John. Diversity, Margins and Non-Stationary Learning.

Degree: 2012, University of Manchester

 Ensemble methods are frequently applied to classification problems, and gen-erally improve upon the performance of individual models. Diversity is consideredto be an important factor in… (more)

…thesis submitted to the University of Manchester for the degree of Doctor of Philosophy, 2012… …x29; and s/he has given The University of Manchester certain rights to use such Copyright… 

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APA (6th Edition):

Stapenhurst, R. J. (2012). Diversity, Margins and Non-Stationary Learning. (Doctoral Dissertation). University of Manchester. Retrieved from http://www.manchester.ac.uk/escholar/uk-ac-man-scw:183554

Chicago Manual of Style (16th Edition):

Stapenhurst, Richard John. “Diversity, Margins and Non-Stationary Learning.” 2012. Doctoral Dissertation, University of Manchester. Accessed June 18, 2019. http://www.manchester.ac.uk/escholar/uk-ac-man-scw:183554.

MLA Handbook (7th Edition):

Stapenhurst, Richard John. “Diversity, Margins and Non-Stationary Learning.” 2012. Web. 18 Jun 2019.

Vancouver:

Stapenhurst RJ. Diversity, Margins and Non-Stationary Learning. [Internet] [Doctoral dissertation]. University of Manchester; 2012. [cited 2019 Jun 18]. Available from: http://www.manchester.ac.uk/escholar/uk-ac-man-scw:183554.

Council of Science Editors:

Stapenhurst RJ. Diversity, Margins and Non-Stationary Learning. [Doctoral Dissertation]. University of Manchester; 2012. Available from: http://www.manchester.ac.uk/escholar/uk-ac-man-scw:183554

10. Mealing, Richard Andrew. Dynamic Opponent Modelling in Two-Player Games.

Degree: 2015, University of Manchester

 This thesis investigates decision-making in two-player imperfect information games against opponents whose actions can affect our rewards, and whose strategies may be based on memories… (more)

Subjects/Keywords: decision-making; imperfect information; learning in games; dynamic opponents; opponent modelling; sequence prediction; change detection; expectation-maximisation; reinforcement learning; lookahead; best-response; Nash equilibrium; self-play convergence; iterated normal-form games; simplified poker; multi-agent learning; game theory

…Richard Andrew Mealing A thesis submitted to the University of Manchester for the degree of… …University of Manchester certain rights to use such Copyright, including for administrative… …Sciences Research Council [grant number EP/P505631/1] as well as the University of… …Manchester for their support. 17 Chapter 1 Introduction Choosing how to act when faced with a… 

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APA (6th Edition):

Mealing, R. A. (2015). Dynamic Opponent Modelling in Two-Player Games. (Doctoral Dissertation). University of Manchester. Retrieved from http://www.manchester.ac.uk/escholar/uk-ac-man-scw:262343

Chicago Manual of Style (16th Edition):

Mealing, Richard Andrew. “Dynamic Opponent Modelling in Two-Player Games.” 2015. Doctoral Dissertation, University of Manchester. Accessed June 18, 2019. http://www.manchester.ac.uk/escholar/uk-ac-man-scw:262343.

MLA Handbook (7th Edition):

Mealing, Richard Andrew. “Dynamic Opponent Modelling in Two-Player Games.” 2015. Web. 18 Jun 2019.

Vancouver:

Mealing RA. Dynamic Opponent Modelling in Two-Player Games. [Internet] [Doctoral dissertation]. University of Manchester; 2015. [cited 2019 Jun 18]. Available from: http://www.manchester.ac.uk/escholar/uk-ac-man-scw:262343.

Council of Science Editors:

Mealing RA. Dynamic Opponent Modelling in Two-Player Games. [Doctoral Dissertation]. University of Manchester; 2015. Available from: http://www.manchester.ac.uk/escholar/uk-ac-man-scw:262343

11. Orhobor, Oghenejokpeme Israel. A General Framework for Building Accurate and Understandable Genomic Models: A Study in Rice (Oryza Sativa).

Degree: 2019, University of Manchester

 Rapid technological advances in genotyping and sequencing technologies are driving the generation of vast amounts of genomic data. These advancements present a unique opportunity to… (more)

…the University of Manchester for the degree of Doctor of Philosophy, 2019 Rapid… …the “Copyright”) and s/he has given The University of Manchester certain rights to use… 

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APA (6th Edition):

Orhobor, O. I. (2019). A General Framework for Building Accurate and Understandable Genomic Models: A Study in Rice (Oryza Sativa). (Doctoral Dissertation). University of Manchester. Retrieved from http://www.manchester.ac.uk/escholar/uk-ac-man-scw:318296

Chicago Manual of Style (16th Edition):

Orhobor, Oghenejokpeme Israel. “A General Framework for Building Accurate and Understandable Genomic Models: A Study in Rice (Oryza Sativa).” 2019. Doctoral Dissertation, University of Manchester. Accessed June 18, 2019. http://www.manchester.ac.uk/escholar/uk-ac-man-scw:318296.

MLA Handbook (7th Edition):

Orhobor, Oghenejokpeme Israel. “A General Framework for Building Accurate and Understandable Genomic Models: A Study in Rice (Oryza Sativa).” 2019. Web. 18 Jun 2019.

Vancouver:

Orhobor OI. A General Framework for Building Accurate and Understandable Genomic Models: A Study in Rice (Oryza Sativa). [Internet] [Doctoral dissertation]. University of Manchester; 2019. [cited 2019 Jun 18]. Available from: http://www.manchester.ac.uk/escholar/uk-ac-man-scw:318296.

Council of Science Editors:

Orhobor OI. A General Framework for Building Accurate and Understandable Genomic Models: A Study in Rice (Oryza Sativa). [Doctoral Dissertation]. University of Manchester; 2019. Available from: http://www.manchester.ac.uk/escholar/uk-ac-man-scw:318296

12. Yiapanis, Paraskevas. High Performance Optimizations in Runtime Speculative Parallelization for Multicore Architectures.

Degree: 2013, University of Manchester

 Thread-Level Speculation (TLS) overcomes limitations intrinsic with conservativecompile-time auto-parallelizing tools by extracting parallel threads optimistically andonly ensuring absence of data dependence violations at runtime.A significant… (more)

…Yiapanis A thesis submitted to the University of Manchester for the degree of Doctor of… …x28;the “Copyright”) and s/he has given The University of Manchester certain rights to… …the University of Manchester, work in which the author of this thesis was involved. The… …University of Edinburgh and the University of Manchester. 30 CHAPTER 1. INTRODUCTION Table 1.1… 

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APA (6th Edition):

Yiapanis, P. (2013). High Performance Optimizations in Runtime Speculative Parallelization for Multicore Architectures. (Doctoral Dissertation). University of Manchester. Retrieved from http://www.manchester.ac.uk/escholar/uk-ac-man-scw:205699

Chicago Manual of Style (16th Edition):

Yiapanis, Paraskevas. “High Performance Optimizations in Runtime Speculative Parallelization for Multicore Architectures.” 2013. Doctoral Dissertation, University of Manchester. Accessed June 18, 2019. http://www.manchester.ac.uk/escholar/uk-ac-man-scw:205699.

MLA Handbook (7th Edition):

Yiapanis, Paraskevas. “High Performance Optimizations in Runtime Speculative Parallelization for Multicore Architectures.” 2013. Web. 18 Jun 2019.

Vancouver:

Yiapanis P. High Performance Optimizations in Runtime Speculative Parallelization for Multicore Architectures. [Internet] [Doctoral dissertation]. University of Manchester; 2013. [cited 2019 Jun 18]. Available from: http://www.manchester.ac.uk/escholar/uk-ac-man-scw:205699.

Council of Science Editors:

Yiapanis P. High Performance Optimizations in Runtime Speculative Parallelization for Multicore Architectures. [Doctoral Dissertation]. University of Manchester; 2013. Available from: http://www.manchester.ac.uk/escholar/uk-ac-man-scw:205699

13. Reeve, Henry. Learning in high dimensions with asymmetric costs.

Degree: 2019, University of Manchester

 A classifier making predictions in noisy environments with limited data will inevitably make some mistakes. However, some types of error are more harmful than others.… (more)

Subjects/Keywords: Classification; Machine Learning; Cost sensitive; Neyman Pearson; Minimax; Non-parametric; k nearest neighbours

…and s/he has given The University of Manchester certain rights to use such Copyright… 

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APA (6th Edition):

Reeve, H. (2019). Learning in high dimensions with asymmetric costs. (Doctoral Dissertation). University of Manchester. Retrieved from http://www.manchester.ac.uk/escholar/uk-ac-man-scw:318266

Chicago Manual of Style (16th Edition):

Reeve, Henry. “Learning in high dimensions with asymmetric costs.” 2019. Doctoral Dissertation, University of Manchester. Accessed June 18, 2019. http://www.manchester.ac.uk/escholar/uk-ac-man-scw:318266.

MLA Handbook (7th Edition):

Reeve, Henry. “Learning in high dimensions with asymmetric costs.” 2019. Web. 18 Jun 2019.

Vancouver:

Reeve H. Learning in high dimensions with asymmetric costs. [Internet] [Doctoral dissertation]. University of Manchester; 2019. [cited 2019 Jun 18]. Available from: http://www.manchester.ac.uk/escholar/uk-ac-man-scw:318266.

Council of Science Editors:

Reeve H. Learning in high dimensions with asymmetric costs. [Doctoral Dissertation]. University of Manchester; 2019. Available from: http://www.manchester.ac.uk/escholar/uk-ac-man-scw:318266

14. Pocock, Adam Craig. Feature Selection via Joint Likelihood.

Degree: 2012, University of Manchester

 We study the nature of filter methods for feature selection. In particular, we examine information theoretic approaches to this problem, looking at the literature over… (more)

Subjects/Keywords: machine learning; feature selection; information theory

…C Pocock A thesis submitted to the University of Manchester for the degree of Doctor of… …rights in it (the “Copyright”) and s/he has given The University of Manchester… 

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APA (6th Edition):

Pocock, A. C. (2012). Feature Selection via Joint Likelihood. (Doctoral Dissertation). University of Manchester. Retrieved from http://www.manchester.ac.uk/escholar/uk-ac-man-scw:167066

Chicago Manual of Style (16th Edition):

Pocock, Adam Craig. “Feature Selection via Joint Likelihood.” 2012. Doctoral Dissertation, University of Manchester. Accessed June 18, 2019. http://www.manchester.ac.uk/escholar/uk-ac-man-scw:167066.

MLA Handbook (7th Edition):

Pocock, Adam Craig. “Feature Selection via Joint Likelihood.” 2012. Web. 18 Jun 2019.

Vancouver:

Pocock AC. Feature Selection via Joint Likelihood. [Internet] [Doctoral dissertation]. University of Manchester; 2012. [cited 2019 Jun 18]. Available from: http://www.manchester.ac.uk/escholar/uk-ac-man-scw:167066.

Council of Science Editors:

Pocock AC. Feature Selection via Joint Likelihood. [Doctoral Dissertation]. University of Manchester; 2012. Available from: http://www.manchester.ac.uk/escholar/uk-ac-man-scw:167066

.