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You searched for +publisher:"Georgia Tech" +contributor:("Gray, Alexander"). Showing records 1 – 22 of 22 total matches.

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1. Kim, Seungyeon. Modeling and visualization of version-controlled documents.

Degree: MS, Computing, 2011, Georgia Tech

 Version-controlled documents, such as Wikipedia or program codes in Subversion, demands a novel methodology to be analyzed efficiently. The documents are continually edited by one… (more)

Subjects/Keywords: Document model; Document visualization; Version-controlled document; Wikis (Computer science); Computational linguistics

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APA (6th Edition):

Kim, S. (2011). Modeling and visualization of version-controlled documents. (Masters Thesis). Georgia Tech. Retrieved from http://hdl.handle.net/1853/39603

Chicago Manual of Style (16th Edition):

Kim, Seungyeon. “Modeling and visualization of version-controlled documents.” 2011. Masters Thesis, Georgia Tech. Accessed April 24, 2019. http://hdl.handle.net/1853/39603.

MLA Handbook (7th Edition):

Kim, Seungyeon. “Modeling and visualization of version-controlled documents.” 2011. Web. 24 Apr 2019.

Vancouver:

Kim S. Modeling and visualization of version-controlled documents. [Internet] [Masters thesis]. Georgia Tech; 2011. [cited 2019 Apr 24]. Available from: http://hdl.handle.net/1853/39603.

Council of Science Editors:

Kim S. Modeling and visualization of version-controlled documents. [Masters Thesis]. Georgia Tech; 2011. Available from: http://hdl.handle.net/1853/39603

2. Balasubramanian, Krishnakumar. Learning without labels and nonnegative tensor factorization.

Degree: MS, Computing, 2010, Georgia Tech

 Supervised learning tasks like building a classifier, estimating the error rate of the predictors, are typically performed with labeled data. In most cases, obtaining labeled… (more)

Subjects/Keywords: Unsupervised; Supervised; Latent vatiable; Classification; Regression; Tensor; Nonnegative; Block principal pivoting; ANLS; Machine learning; Artificial intelligence; Supervised learning (Machine learning); Calculus of tensors

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APA (6th Edition):

Balasubramanian, K. (2010). Learning without labels and nonnegative tensor factorization. (Masters Thesis). Georgia Tech. Retrieved from http://hdl.handle.net/1853/33926

Chicago Manual of Style (16th Edition):

Balasubramanian, Krishnakumar. “Learning without labels and nonnegative tensor factorization.” 2010. Masters Thesis, Georgia Tech. Accessed April 24, 2019. http://hdl.handle.net/1853/33926.

MLA Handbook (7th Edition):

Balasubramanian, Krishnakumar. “Learning without labels and nonnegative tensor factorization.” 2010. Web. 24 Apr 2019.

Vancouver:

Balasubramanian K. Learning without labels and nonnegative tensor factorization. [Internet] [Masters thesis]. Georgia Tech; 2010. [cited 2019 Apr 24]. Available from: http://hdl.handle.net/1853/33926.

Council of Science Editors:

Balasubramanian K. Learning without labels and nonnegative tensor factorization. [Masters Thesis]. Georgia Tech; 2010. Available from: http://hdl.handle.net/1853/33926


Georgia Tech

3. Mehta, Nishant A. On sparse representations and new meta-learning paradigms for representation learning.

Degree: PhD, Computer Science, 2013, Georgia Tech

 Given the "right" representation, learning is easy. This thesis studies representation learning and meta-learning, with a special focus on sparse representations. Meta-learning is fundamental to… (more)

Subjects/Keywords: Learning theory; Data-dependent complexity; Luckiness; Dictionary learning; Sparse coding; Lasso; Multi-task learning; Meta-learning; Learning to learn

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APA (6th Edition):

Mehta, N. A. (2013). On sparse representations and new meta-learning paradigms for representation learning. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/52159

Chicago Manual of Style (16th Edition):

Mehta, Nishant A. “On sparse representations and new meta-learning paradigms for representation learning.” 2013. Doctoral Dissertation, Georgia Tech. Accessed April 24, 2019. http://hdl.handle.net/1853/52159.

MLA Handbook (7th Edition):

Mehta, Nishant A. “On sparse representations and new meta-learning paradigms for representation learning.” 2013. Web. 24 Apr 2019.

Vancouver:

Mehta NA. On sparse representations and new meta-learning paradigms for representation learning. [Internet] [Doctoral dissertation]. Georgia Tech; 2013. [cited 2019 Apr 24]. Available from: http://hdl.handle.net/1853/52159.

Council of Science Editors:

Mehta NA. On sparse representations and new meta-learning paradigms for representation learning. [Doctoral Dissertation]. Georgia Tech; 2013. Available from: http://hdl.handle.net/1853/52159


Georgia Tech

4. Tariq, Muhammad Mukarram Bin. Modeling performance of internet-based services using causal reasoning.

Degree: PhD, Computing, 2010, Georgia Tech

 The performance of Internet-based services depends on many server-side, client-side, and network related factors. Often, the interaction among the factors or their effect on service… (more)

Subjects/Keywords: CDN; Network neutrality; Causal reasoning; Performance models; Content distribution networks; Causality; Internet programming; Quality assurance; Mathematical models

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APA (6th Edition):

Tariq, M. M. B. (2010). Modeling performance of internet-based services using causal reasoning. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/33927

Chicago Manual of Style (16th Edition):

Tariq, Muhammad Mukarram Bin. “Modeling performance of internet-based services using causal reasoning.” 2010. Doctoral Dissertation, Georgia Tech. Accessed April 24, 2019. http://hdl.handle.net/1853/33927.

MLA Handbook (7th Edition):

Tariq, Muhammad Mukarram Bin. “Modeling performance of internet-based services using causal reasoning.” 2010. Web. 24 Apr 2019.

Vancouver:

Tariq MMB. Modeling performance of internet-based services using causal reasoning. [Internet] [Doctoral dissertation]. Georgia Tech; 2010. [cited 2019 Apr 24]. Available from: http://hdl.handle.net/1853/33927.

Council of Science Editors:

Tariq MMB. Modeling performance of internet-based services using causal reasoning. [Doctoral Dissertation]. Georgia Tech; 2010. Available from: http://hdl.handle.net/1853/33927


Georgia Tech

5. Jiang, Huijing. Statistical computation and inference for functional data analysis.

Degree: PhD, Industrial and Systems Engineering, 2010, Georgia Tech

 My doctoral research dissertation focuses on two aspects of functional data analysis (FDA): FDA under spatial interdependence and FDA for multi-level data. The first part… (more)

Subjects/Keywords: Service distribution equity; Multi-level data; Model-based clustering; Spatio-temporal; Functional data analysis; Multilevel models (Statistics); Markov random fields

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APA (6th Edition):

Jiang, H. (2010). Statistical computation and inference for functional data analysis. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/37087

Chicago Manual of Style (16th Edition):

Jiang, Huijing. “Statistical computation and inference for functional data analysis.” 2010. Doctoral Dissertation, Georgia Tech. Accessed April 24, 2019. http://hdl.handle.net/1853/37087.

MLA Handbook (7th Edition):

Jiang, Huijing. “Statistical computation and inference for functional data analysis.” 2010. Web. 24 Apr 2019.

Vancouver:

Jiang H. Statistical computation and inference for functional data analysis. [Internet] [Doctoral dissertation]. Georgia Tech; 2010. [cited 2019 Apr 24]. Available from: http://hdl.handle.net/1853/37087.

Council of Science Editors:

Jiang H. Statistical computation and inference for functional data analysis. [Doctoral Dissertation]. Georgia Tech; 2010. Available from: http://hdl.handle.net/1853/37087


Georgia Tech

6. Bian, Jiang. Contextualized web search: query-dependent ranking and social media search.

Degree: PhD, Computing, 2010, Georgia Tech

 Due to the information explosion on the Internet, effective information search techniques are required to retrieve the desired information from the Web. Based on much… (more)

Subjects/Keywords: Social media; Ranking model; Web search; Web search engines

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APA (6th Edition):

Bian, J. (2010). Contextualized web search: query-dependent ranking and social media search. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/37246

Chicago Manual of Style (16th Edition):

Bian, Jiang. “Contextualized web search: query-dependent ranking and social media search.” 2010. Doctoral Dissertation, Georgia Tech. Accessed April 24, 2019. http://hdl.handle.net/1853/37246.

MLA Handbook (7th Edition):

Bian, Jiang. “Contextualized web search: query-dependent ranking and social media search.” 2010. Web. 24 Apr 2019.

Vancouver:

Bian J. Contextualized web search: query-dependent ranking and social media search. [Internet] [Doctoral dissertation]. Georgia Tech; 2010. [cited 2019 Apr 24]. Available from: http://hdl.handle.net/1853/37246.

Council of Science Editors:

Bian J. Contextualized web search: query-dependent ranking and social media search. [Doctoral Dissertation]. Georgia Tech; 2010. Available from: http://hdl.handle.net/1853/37246

7. Ganti Mahapatruni, Ravi Sastry. New formulations for active learning.

Degree: PhD, Computer Science, 2014, Georgia Tech

 In this thesis, we provide computationally efficient algorithms with provable statistical guarantees, for the problem of active learning, by using ideas from sequential analysis. We… (more)

Subjects/Keywords: Active learning; Sequential analysis; Stochastic optimization; Active learning; Algorithms; Sequential analysis; Mathematical optimization; Machine learning

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APA (6th Edition):

Ganti Mahapatruni, R. S. (2014). New formulations for active learning. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/51801

Chicago Manual of Style (16th Edition):

Ganti Mahapatruni, Ravi Sastry. “New formulations for active learning.” 2014. Doctoral Dissertation, Georgia Tech. Accessed April 24, 2019. http://hdl.handle.net/1853/51801.

MLA Handbook (7th Edition):

Ganti Mahapatruni, Ravi Sastry. “New formulations for active learning.” 2014. Web. 24 Apr 2019.

Vancouver:

Ganti Mahapatruni RS. New formulations for active learning. [Internet] [Doctoral dissertation]. Georgia Tech; 2014. [cited 2019 Apr 24]. Available from: http://hdl.handle.net/1853/51801.

Council of Science Editors:

Ganti Mahapatruni RS. New formulations for active learning. [Doctoral Dissertation]. Georgia Tech; 2014. Available from: http://hdl.handle.net/1853/51801

8. Guan, Wei. New support vector machine formulations and algorithms with application to biomedical data analysis.

Degree: PhD, Computing, 2011, Georgia Tech

 The Support Vector Machine (SVM) classifier seeks to find the separating hyperplane wx=r that maximizes the margin distance 1/||w||22. It can be formalized as an… (more)

Subjects/Keywords: Ovarian cancer detection; Functional SVM; Biomarker discovery; Mixed-integer SVM; Fractional-norm SVM; Non-negative SVM; Ranking SVM; Protein folding energy function; Support vector machine optimization; Support vector machines; Algorithms; Bioinformatics; Machine learning

Georgia Tech after approval by the Institutional Review Board from Northside Hospital and… 

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APA (6th Edition):

Guan, W. (2011). New support vector machine formulations and algorithms with application to biomedical data analysis. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/41126

Chicago Manual of Style (16th Edition):

Guan, Wei. “New support vector machine formulations and algorithms with application to biomedical data analysis.” 2011. Doctoral Dissertation, Georgia Tech. Accessed April 24, 2019. http://hdl.handle.net/1853/41126.

MLA Handbook (7th Edition):

Guan, Wei. “New support vector machine formulations and algorithms with application to biomedical data analysis.” 2011. Web. 24 Apr 2019.

Vancouver:

Guan W. New support vector machine formulations and algorithms with application to biomedical data analysis. [Internet] [Doctoral dissertation]. Georgia Tech; 2011. [cited 2019 Apr 24]. Available from: http://hdl.handle.net/1853/41126.

Council of Science Editors:

Guan W. New support vector machine formulations and algorithms with application to biomedical data analysis. [Doctoral Dissertation]. Georgia Tech; 2011. Available from: http://hdl.handle.net/1853/41126

9. Cunial, Fabio. Analysis of the subsequence composition of biosequences.

Degree: PhD, Computing, 2012, Georgia Tech

 Measuring the amount of information and of shared information in biological strings, as well as relating information to structure, function and evolution, are fundamental computational… (more)

Subjects/Keywords: Subsequences; Compositional complexity; Phylogeny reconstruction; Alignment-free sequence comparison; Sparse motifs; LZW; LZWA; Variance computation; Protein domains; Proteomes; Phylogeny; Polypeptides; Molecular biology; Algorithms

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APA (6th Edition):

Cunial, F. (2012). Analysis of the subsequence composition of biosequences. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/44716

Chicago Manual of Style (16th Edition):

Cunial, Fabio. “Analysis of the subsequence composition of biosequences.” 2012. Doctoral Dissertation, Georgia Tech. Accessed April 24, 2019. http://hdl.handle.net/1853/44716.

MLA Handbook (7th Edition):

Cunial, Fabio. “Analysis of the subsequence composition of biosequences.” 2012. Web. 24 Apr 2019.

Vancouver:

Cunial F. Analysis of the subsequence composition of biosequences. [Internet] [Doctoral dissertation]. Georgia Tech; 2012. [cited 2019 Apr 24]. Available from: http://hdl.handle.net/1853/44716.

Council of Science Editors:

Cunial F. Analysis of the subsequence composition of biosequences. [Doctoral Dissertation]. Georgia Tech; 2012. Available from: http://hdl.handle.net/1853/44716

10. Lee, Dong Ryeol. A distributed kernel summation framework for machine learning and scientific applications.

Degree: PhD, Computing, 2012, Georgia Tech

 The class of computational problems I consider in this thesis share the common trait of requiring consideration of pairs (or higher-order tuples) of data points.… (more)

Subjects/Keywords: Distributed and shared memory parallelism; Parallel multitree methods; Fast Gauss transforms; Fast multipole methods; Parallel machine learning; Parallel kernel methods; Multidimensional trees; Kernel functions; Machine learning; Algorithms

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APA (6th Edition):

Lee, D. R. (2012). A distributed kernel summation framework for machine learning and scientific applications. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/44727

Chicago Manual of Style (16th Edition):

Lee, Dong Ryeol. “A distributed kernel summation framework for machine learning and scientific applications.” 2012. Doctoral Dissertation, Georgia Tech. Accessed April 24, 2019. http://hdl.handle.net/1853/44727.

MLA Handbook (7th Edition):

Lee, Dong Ryeol. “A distributed kernel summation framework for machine learning and scientific applications.” 2012. Web. 24 Apr 2019.

Vancouver:

Lee DR. A distributed kernel summation framework for machine learning and scientific applications. [Internet] [Doctoral dissertation]. Georgia Tech; 2012. [cited 2019 Apr 24]. Available from: http://hdl.handle.net/1853/44727.

Council of Science Editors:

Lee DR. A distributed kernel summation framework for machine learning and scientific applications. [Doctoral Dissertation]. Georgia Tech; 2012. Available from: http://hdl.handle.net/1853/44727

11. Kim, Jingu. Nonnegative matrix and tensor factorizations, least squares problems, and applications.

Degree: PhD, Computing, 2011, Georgia Tech

 Nonnegative matrix factorization (NMF) is a useful dimension reduction method that has been investigated and applied in various areas. NMF is considered for high-dimensional data… (more)

Subjects/Keywords: Linear complementarity problem; Parallel factorization; Canonical decomposition; Active set method; Rank deficiency; l1-regularized linear regression; Mixed-norm regularization; Low rank approximation; Block principal pivoting; Nonnegativity constrained least squares; Computer science; Matrices; Least squares

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APA (6th Edition):

Kim, J. (2011). Nonnegative matrix and tensor factorizations, least squares problems, and applications. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/42909

Chicago Manual of Style (16th Edition):

Kim, Jingu. “Nonnegative matrix and tensor factorizations, least squares problems, and applications.” 2011. Doctoral Dissertation, Georgia Tech. Accessed April 24, 2019. http://hdl.handle.net/1853/42909.

MLA Handbook (7th Edition):

Kim, Jingu. “Nonnegative matrix and tensor factorizations, least squares problems, and applications.” 2011. Web. 24 Apr 2019.

Vancouver:

Kim J. Nonnegative matrix and tensor factorizations, least squares problems, and applications. [Internet] [Doctoral dissertation]. Georgia Tech; 2011. [cited 2019 Apr 24]. Available from: http://hdl.handle.net/1853/42909.

Council of Science Editors:

Kim J. Nonnegative matrix and tensor factorizations, least squares problems, and applications. [Doctoral Dissertation]. Georgia Tech; 2011. Available from: http://hdl.handle.net/1853/42909

12. Ouyang, Hua. Optimal stochastic and distributed algorithms for machine learning.

Degree: PhD, Computer Science, 2013, Georgia Tech

 Stochastic and data-distributed optimization algorithms have received lots of attention from the machine learning community due to the tremendous demand from the large-scale learning and… (more)

Subjects/Keywords: Machine learning; BigData; Optimization; Stochastic optimization; Convergence rate; Distributed learning; Optimal methods; ADMM; Kernel method; SVM; Machine learning; Computer algorithms; Mathematical optimization

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APA (6th Edition):

Ouyang, H. (2013). Optimal stochastic and distributed algorithms for machine learning. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/49091

Chicago Manual of Style (16th Edition):

Ouyang, Hua. “Optimal stochastic and distributed algorithms for machine learning.” 2013. Doctoral Dissertation, Georgia Tech. Accessed April 24, 2019. http://hdl.handle.net/1853/49091.

MLA Handbook (7th Edition):

Ouyang, Hua. “Optimal stochastic and distributed algorithms for machine learning.” 2013. Web. 24 Apr 2019.

Vancouver:

Ouyang H. Optimal stochastic and distributed algorithms for machine learning. [Internet] [Doctoral dissertation]. Georgia Tech; 2013. [cited 2019 Apr 24]. Available from: http://hdl.handle.net/1853/49091.

Council of Science Editors:

Ouyang H. Optimal stochastic and distributed algorithms for machine learning. [Doctoral Dissertation]. Georgia Tech; 2013. Available from: http://hdl.handle.net/1853/49091

13. Riegel, Ryan Nelson. Generalized N-body problems: a framework for scalable computation.

Degree: PhD, Computational Science and Engineering, 2013, Georgia Tech

 In the wake of the Big Data phenomenon, the computing world has seen a number of computational paradigms developed in response to the sudden need… (more)

Subjects/Keywords: Fast algorithms; Generalized algorithms; Tree codes; Complexity analysis; Database-resident computation; Machine learning; Nearest neighbors; Kernel sums; Affinity propagation; Kernel discriminant analysis; Quasar identification; Big data; Parallel processing (Electronic computers); Many-body problem; Algorithms

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APA (6th Edition):

Riegel, R. N. (2013). Generalized N-body problems: a framework for scalable computation. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/50269

Chicago Manual of Style (16th Edition):

Riegel, Ryan Nelson. “Generalized N-body problems: a framework for scalable computation.” 2013. Doctoral Dissertation, Georgia Tech. Accessed April 24, 2019. http://hdl.handle.net/1853/50269.

MLA Handbook (7th Edition):

Riegel, Ryan Nelson. “Generalized N-body problems: a framework for scalable computation.” 2013. Web. 24 Apr 2019.

Vancouver:

Riegel RN. Generalized N-body problems: a framework for scalable computation. [Internet] [Doctoral dissertation]. Georgia Tech; 2013. [cited 2019 Apr 24]. Available from: http://hdl.handle.net/1853/50269.

Council of Science Editors:

Riegel RN. Generalized N-body problems: a framework for scalable computation. [Doctoral Dissertation]. Georgia Tech; 2013. Available from: http://hdl.handle.net/1853/50269

14. Choo, Jae gul. Integration of computational methods and visual analytics for large-scale high-dimensional data.

Degree: PhD, Computational Science and Engineering, 2013, Georgia Tech

 With the increasing amount of collected data, large-scale high-dimensional data analysis is becoming essential in many areas. These data can be analyzed either by using… (more)

Subjects/Keywords: Dimension reduction; Clustering; High-dimensional data; Visualization; Visual analytics; Dimensional analysis; Data structures (Computer science); Information visualization; Visual analytics; Mathematical statistics Data processing

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APA (6th Edition):

Choo, J. g. (2013). Integration of computational methods and visual analytics for large-scale high-dimensional data. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/49121

Chicago Manual of Style (16th Edition):

Choo, Jae gul. “Integration of computational methods and visual analytics for large-scale high-dimensional data.” 2013. Doctoral Dissertation, Georgia Tech. Accessed April 24, 2019. http://hdl.handle.net/1853/49121.

MLA Handbook (7th Edition):

Choo, Jae gul. “Integration of computational methods and visual analytics for large-scale high-dimensional data.” 2013. Web. 24 Apr 2019.

Vancouver:

Choo Jg. Integration of computational methods and visual analytics for large-scale high-dimensional data. [Internet] [Doctoral dissertation]. Georgia Tech; 2013. [cited 2019 Apr 24]. Available from: http://hdl.handle.net/1853/49121.

Council of Science Editors:

Choo Jg. Integration of computational methods and visual analytics for large-scale high-dimensional data. [Doctoral Dissertation]. Georgia Tech; 2013. Available from: http://hdl.handle.net/1853/49121

15. Crain, Steven P. Personalized search and recommendation for health information resources.

Degree: PhD, Computational Science and Engineering, 2012, Georgia Tech

 Consumers face several challenges using the Internet to fill health-related needs. (1) In many cases, they face a language gap as they look for information… (more)

Subjects/Keywords: Recommender systems; Information retrieval; Health informatics; Consumer health; Social computing; Social media; Communication; Medical informatics; Information resources; Web browsing

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APA (6th Edition):

Crain, S. P. (2012). Personalized search and recommendation for health information resources. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/45805

Chicago Manual of Style (16th Edition):

Crain, Steven P. “Personalized search and recommendation for health information resources.” 2012. Doctoral Dissertation, Georgia Tech. Accessed April 24, 2019. http://hdl.handle.net/1853/45805.

MLA Handbook (7th Edition):

Crain, Steven P. “Personalized search and recommendation for health information resources.” 2012. Web. 24 Apr 2019.

Vancouver:

Crain SP. Personalized search and recommendation for health information resources. [Internet] [Doctoral dissertation]. Georgia Tech; 2012. [cited 2019 Apr 24]. Available from: http://hdl.handle.net/1853/45805.

Council of Science Editors:

Crain SP. Personalized search and recommendation for health information resources. [Doctoral Dissertation]. Georgia Tech; 2012. Available from: http://hdl.handle.net/1853/45805

16. Tran, Long Quoc. Efficient inference algorithms for network activities.

Degree: PhD, Computational Science and Engineering, 2015, Georgia Tech

 The real social network and associated communities are often hidden under the declared friend or group lists in social networks. We usually observe the manifestation… (more)

Subjects/Keywords: Hawkes; Inference

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APA (6th Edition):

Tran, L. Q. (2015). Efficient inference algorithms for network activities. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/53499

Chicago Manual of Style (16th Edition):

Tran, Long Quoc. “Efficient inference algorithms for network activities.” 2015. Doctoral Dissertation, Georgia Tech. Accessed April 24, 2019. http://hdl.handle.net/1853/53499.

MLA Handbook (7th Edition):

Tran, Long Quoc. “Efficient inference algorithms for network activities.” 2015. Web. 24 Apr 2019.

Vancouver:

Tran LQ. Efficient inference algorithms for network activities. [Internet] [Doctoral dissertation]. Georgia Tech; 2015. [cited 2019 Apr 24]. Available from: http://hdl.handle.net/1853/53499.

Council of Science Editors:

Tran LQ. Efficient inference algorithms for network activities. [Doctoral Dissertation]. Georgia Tech; 2015. Available from: http://hdl.handle.net/1853/53499

17. Bhat, Sooraj. Syntactic foundations for machine learning.

Degree: PhD, Computer Science, 2013, Georgia Tech

 Machine learning has risen in importance across science, engineering, and business in recent years. Domain experts have begun to understand how their data analysis problems… (more)

Subjects/Keywords: Probabilistic programming; Type theory; Formal languages; Probability; Optimization; Semantics; Machine learning; Stochastic models; Computer programming

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APA (6th Edition):

Bhat, S. (2013). Syntactic foundations for machine learning. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/47700

Chicago Manual of Style (16th Edition):

Bhat, Sooraj. “Syntactic foundations for machine learning.” 2013. Doctoral Dissertation, Georgia Tech. Accessed April 24, 2019. http://hdl.handle.net/1853/47700.

MLA Handbook (7th Edition):

Bhat, Sooraj. “Syntactic foundations for machine learning.” 2013. Web. 24 Apr 2019.

Vancouver:

Bhat S. Syntactic foundations for machine learning. [Internet] [Doctoral dissertation]. Georgia Tech; 2013. [cited 2019 Apr 24]. Available from: http://hdl.handle.net/1853/47700.

Council of Science Editors:

Bhat S. Syntactic foundations for machine learning. [Doctoral Dissertation]. Georgia Tech; 2013. Available from: http://hdl.handle.net/1853/47700

18. Ram, Parikshit. New paradigms for approximate nearest-neighbor search.

Degree: PhD, Computational Science and Engineering, 2013, Georgia Tech

 Nearest-neighbor search is a very natural and universal problem in computer science. Often times, the problem size necessitates approximation. In this thesis, I present new… (more)

Subjects/Keywords: Similarity search; Nearest-neighbor search; Computational geometry; Algorithms and analysis; Nearest neighbor analysis (Statistics); Approximation algorithms; Search theory

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APA (6th Edition):

Ram, P. (2013). New paradigms for approximate nearest-neighbor search. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/49112

Chicago Manual of Style (16th Edition):

Ram, Parikshit. “New paradigms for approximate nearest-neighbor search.” 2013. Doctoral Dissertation, Georgia Tech. Accessed April 24, 2019. http://hdl.handle.net/1853/49112.

MLA Handbook (7th Edition):

Ram, Parikshit. “New paradigms for approximate nearest-neighbor search.” 2013. Web. 24 Apr 2019.

Vancouver:

Ram P. New paradigms for approximate nearest-neighbor search. [Internet] [Doctoral dissertation]. Georgia Tech; 2013. [cited 2019 Apr 24]. Available from: http://hdl.handle.net/1853/49112.

Council of Science Editors:

Ram P. New paradigms for approximate nearest-neighbor search. [Doctoral Dissertation]. Georgia Tech; 2013. Available from: http://hdl.handle.net/1853/49112

19. Baah, George Kofi. Statistical causal analysis for fault localization.

Degree: PhD, Computing, 2012, Georgia Tech

 The ubiquitous nature of software demands that software is released without faults. However, software developers inadvertently introduce faults into software during development. To remove the… (more)

Subjects/Keywords: Causal analysis; Probabilistic graphical models; Fault localization; Debugging; Program analysis; Probabilities; Software engineering; Computer software Development; Computer software Quality control

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

APA (6th Edition):

Baah, G. K. (2012). Statistical causal analysis for fault localization. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/45762

Chicago Manual of Style (16th Edition):

Baah, George Kofi. “Statistical causal analysis for fault localization.” 2012. Doctoral Dissertation, Georgia Tech. Accessed April 24, 2019. http://hdl.handle.net/1853/45762.

MLA Handbook (7th Edition):

Baah, George Kofi. “Statistical causal analysis for fault localization.” 2012. Web. 24 Apr 2019.

Vancouver:

Baah GK. Statistical causal analysis for fault localization. [Internet] [Doctoral dissertation]. Georgia Tech; 2012. [cited 2019 Apr 24]. Available from: http://hdl.handle.net/1853/45762.

Council of Science Editors:

Baah GK. Statistical causal analysis for fault localization. [Doctoral Dissertation]. Georgia Tech; 2012. Available from: http://hdl.handle.net/1853/45762

20. Sun, Mingxuan. Visualizing and modeling partial incomplete ranking data.

Degree: PhD, Computing, 2012, Georgia Tech

 Analyzing ranking data is an essential component in a wide range of important applications including web-search and recommendation systems. Rankings are difficult to visualize or… (more)

Subjects/Keywords: Recommender systems; Weighted hoeffding distance; Kernel smoothing; Search algorithm dissimilarity; Partial incomplete ranking; Algorithms; Ranking and selection (Statistics)

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

APA (6th Edition):

Sun, M. (2012). Visualizing and modeling partial incomplete ranking data. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/45793

Chicago Manual of Style (16th Edition):

Sun, Mingxuan. “Visualizing and modeling partial incomplete ranking data.” 2012. Doctoral Dissertation, Georgia Tech. Accessed April 24, 2019. http://hdl.handle.net/1853/45793.

MLA Handbook (7th Edition):

Sun, Mingxuan. “Visualizing and modeling partial incomplete ranking data.” 2012. Web. 24 Apr 2019.

Vancouver:

Sun M. Visualizing and modeling partial incomplete ranking data. [Internet] [Doctoral dissertation]. Georgia Tech; 2012. [cited 2019 Apr 24]. Available from: http://hdl.handle.net/1853/45793.

Council of Science Editors:

Sun M. Visualizing and modeling partial incomplete ranking data. [Doctoral Dissertation]. Georgia Tech; 2012. Available from: http://hdl.handle.net/1853/45793

21. March, William B. Multi-tree algorithms for computational statistics and phyiscs.

Degree: PhD, Computational Science and Engineering, 2013, Georgia Tech

 The Fast Multipole Method of Greengard and Rokhlin does the seemingly impossible: it approximates the quadratic scaling N-body problem in linear time. The key is… (more)

Subjects/Keywords: Multi-tree algorithms; N-point correlation functions; Hartree-Fock theory; Computer algorithms; Combinatorial analysis; Hartree-Fock approximation

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

APA (6th Edition):

March, W. B. (2013). Multi-tree algorithms for computational statistics and phyiscs. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/49116

Chicago Manual of Style (16th Edition):

March, William B. “Multi-tree algorithms for computational statistics and phyiscs.” 2013. Doctoral Dissertation, Georgia Tech. Accessed April 24, 2019. http://hdl.handle.net/1853/49116.

MLA Handbook (7th Edition):

March, William B. “Multi-tree algorithms for computational statistics and phyiscs.” 2013. Web. 24 Apr 2019.

Vancouver:

March WB. Multi-tree algorithms for computational statistics and phyiscs. [Internet] [Doctoral dissertation]. Georgia Tech; 2013. [cited 2019 Apr 24]. Available from: http://hdl.handle.net/1853/49116.

Council of Science Editors:

March WB. Multi-tree algorithms for computational statistics and phyiscs. [Doctoral Dissertation]. Georgia Tech; 2013. Available from: http://hdl.handle.net/1853/49116


Georgia Tech

22. Lee, Teahyung. Algorithm-Based Efficient Approaches for Motion Estimation Systems.

Degree: PhD, Electrical and Computer Engineering, 2007, Georgia Tech

 Algorithm-Based Efficient Approaches for Motion Estimation Systems Teahyung Lee 121 pages Directed by Dr. David V. Anderson This research addresses algorithms for efficient motion estimation… (more)

Subjects/Keywords: Least-squares; Optical flow; Recursive least-squares; Multi-resolution; Image sensor; Motion estimation; Video compression; Coding theory; Algorithms; Motion Measurement

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

APA (6th Edition):

Lee, T. (2007). Algorithm-Based Efficient Approaches for Motion Estimation Systems. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/19783

Chicago Manual of Style (16th Edition):

Lee, Teahyung. “Algorithm-Based Efficient Approaches for Motion Estimation Systems.” 2007. Doctoral Dissertation, Georgia Tech. Accessed April 24, 2019. http://hdl.handle.net/1853/19783.

MLA Handbook (7th Edition):

Lee, Teahyung. “Algorithm-Based Efficient Approaches for Motion Estimation Systems.” 2007. Web. 24 Apr 2019.

Vancouver:

Lee T. Algorithm-Based Efficient Approaches for Motion Estimation Systems. [Internet] [Doctoral dissertation]. Georgia Tech; 2007. [cited 2019 Apr 24]. Available from: http://hdl.handle.net/1853/19783.

Council of Science Editors:

Lee T. Algorithm-Based Efficient Approaches for Motion Estimation Systems. [Doctoral Dissertation]. Georgia Tech; 2007. Available from: http://hdl.handle.net/1853/19783

.