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

Degree: MS, Computing, 2011, Georgia Tech

URL: http://hdl.handle.net/1853/39603

► 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 (6^{th} 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 (16^{th} Edition):

Kim, Seungyeon. “Modeling and visualization of version-controlled documents.” 2011. Masters Thesis, Georgia Tech. Accessed October 20, 2019. http://hdl.handle.net/1853/39603.

MLA Handbook (7^{th} Edition):

Kim, Seungyeon. “Modeling and visualization of version-controlled documents.” 2011. Web. 20 Oct 2019.

Vancouver:

Kim S. Modeling and visualization of version-controlled documents. [Internet] [Masters thesis]. Georgia Tech; 2011. [cited 2019 Oct 20]. 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

URL: http://hdl.handle.net/1853/33926

► 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 (6^{th} 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 (16^{th} Edition):

Balasubramanian, Krishnakumar. “Learning without labels and nonnegative tensor factorization.” 2010. Masters Thesis, Georgia Tech. Accessed October 20, 2019. http://hdl.handle.net/1853/33926.

MLA Handbook (7^{th} Edition):

Balasubramanian, Krishnakumar. “Learning without labels and nonnegative tensor factorization.” 2010. Web. 20 Oct 2019.

Vancouver:

Balasubramanian K. Learning without labels and nonnegative tensor factorization. [Internet] [Masters thesis]. Georgia Tech; 2010. [cited 2019 Oct 20]. 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

URL: http://hdl.handle.net/1853/52159

► 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 (6^{th} 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 (16^{th} Edition):

Mehta, Nishant A. “On sparse representations and new meta-learning paradigms for representation learning.” 2013. Doctoral Dissertation, Georgia Tech. Accessed October 20, 2019. http://hdl.handle.net/1853/52159.

MLA Handbook (7^{th} Edition):

Mehta, Nishant A. “On sparse representations and new meta-learning paradigms for representation learning.” 2013. Web. 20 Oct 2019.

Vancouver:

Mehta NA. On sparse representations and new meta-learning paradigms for representation learning. [Internet] [Doctoral dissertation]. Georgia Tech; 2013. [cited 2019 Oct 20]. 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

URL: http://hdl.handle.net/1853/33927

► 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 (6^{th} 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 (16^{th} Edition):

Tariq, Muhammad Mukarram Bin. “Modeling performance of internet-based services using causal reasoning.” 2010. Doctoral Dissertation, Georgia Tech. Accessed October 20, 2019. http://hdl.handle.net/1853/33927.

MLA Handbook (7^{th} Edition):

Tariq, Muhammad Mukarram Bin. “Modeling performance of internet-based services using causal reasoning.” 2010. Web. 20 Oct 2019.

Vancouver:

Tariq MMB. Modeling performance of internet-based services using causal reasoning. [Internet] [Doctoral dissertation]. Georgia Tech; 2010. [cited 2019 Oct 20]. 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

URL: http://hdl.handle.net/1853/37087

► 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 (6^{th} 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 (16^{th} Edition):

Jiang, Huijing. “Statistical computation and inference for functional data analysis.” 2010. Doctoral Dissertation, Georgia Tech. Accessed October 20, 2019. http://hdl.handle.net/1853/37087.

MLA Handbook (7^{th} Edition):

Jiang, Huijing. “Statistical computation and inference for functional data analysis.” 2010. Web. 20 Oct 2019.

Vancouver:

Jiang H. Statistical computation and inference for functional data analysis. [Internet] [Doctoral dissertation]. Georgia Tech; 2010. [cited 2019 Oct 20]. 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

URL: http://hdl.handle.net/1853/37246

► 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 (6^{th} 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 (16^{th} Edition):

Bian, Jiang. “Contextualized web search: query-dependent ranking and social media search.” 2010. Doctoral Dissertation, Georgia Tech. Accessed October 20, 2019. http://hdl.handle.net/1853/37246.

MLA Handbook (7^{th} Edition):

Bian, Jiang. “Contextualized web search: query-dependent ranking and social media search.” 2010. Web. 20 Oct 2019.

Vancouver:

Bian J. Contextualized web search: query-dependent ranking and social media search. [Internet] [Doctoral dissertation]. Georgia Tech; 2010. [cited 2019 Oct 20]. 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

URL: http://hdl.handle.net/1853/51801

► 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 (6^{th} 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 (16^{th} Edition):

Ganti Mahapatruni, Ravi Sastry. “New formulations for active learning.” 2014. Doctoral Dissertation, Georgia Tech. Accessed October 20, 2019. http://hdl.handle.net/1853/51801.

MLA Handbook (7^{th} Edition):

Ganti Mahapatruni, Ravi Sastry. “New formulations for active learning.” 2014. Web. 20 Oct 2019.

Vancouver:

Ganti Mahapatruni RS. New formulations for active learning. [Internet] [Doctoral dissertation]. Georgia Tech; 2014. [cited 2019 Oct 20]. 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

URL: http://hdl.handle.net/1853/41126

► The Support Vector Machine (SVM) classifier seeks to find the separating hyperplane wx=r that maximizes the margin distance 1/||w||2^{2}. 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 (6^{th} 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 (16^{th} Edition):

Guan, Wei. “New support vector machine formulations and algorithms with application to biomedical data analysis.” 2011. Doctoral Dissertation, Georgia Tech. Accessed October 20, 2019. http://hdl.handle.net/1853/41126.

MLA Handbook (7^{th} Edition):

Guan, Wei. “New support vector machine formulations and algorithms with application to biomedical data analysis.” 2011. Web. 20 Oct 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 Oct 20]. 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

URL: http://hdl.handle.net/1853/44716

► 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 (6^{th} 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 (16^{th} Edition):

Cunial, Fabio. “Analysis of the subsequence composition of biosequences.” 2012. Doctoral Dissertation, Georgia Tech. Accessed October 20, 2019. http://hdl.handle.net/1853/44716.

MLA Handbook (7^{th} Edition):

Cunial, Fabio. “Analysis of the subsequence composition of biosequences.” 2012. Web. 20 Oct 2019.

Vancouver:

Cunial F. Analysis of the subsequence composition of biosequences. [Internet] [Doctoral dissertation]. Georgia Tech; 2012. [cited 2019 Oct 20]. 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

URL: http://hdl.handle.net/1853/44727

► 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 (6^{th} 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 (16^{th} Edition):

Lee, Dong Ryeol. “A distributed kernel summation framework for machine learning and scientific applications.” 2012. Doctoral Dissertation, Georgia Tech. Accessed October 20, 2019. http://hdl.handle.net/1853/44727.

MLA Handbook (7^{th} Edition):

Lee, Dong Ryeol. “A distributed kernel summation framework for machine learning and scientific applications.” 2012. Web. 20 Oct 2019.

Vancouver:

Lee DR. A distributed kernel summation framework for machine learning and scientific applications. [Internet] [Doctoral dissertation]. Georgia Tech; 2012. [cited 2019 Oct 20]. 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

URL: http://hdl.handle.net/1853/42909

► 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 (6^{th} 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 (16^{th} Edition):

Kim, Jingu. “Nonnegative matrix and tensor factorizations, least squares problems, and applications.” 2011. Doctoral Dissertation, Georgia Tech. Accessed October 20, 2019. http://hdl.handle.net/1853/42909.

MLA Handbook (7^{th} Edition):

Kim, Jingu. “Nonnegative matrix and tensor factorizations, least squares problems, and applications.” 2011. Web. 20 Oct 2019.

Vancouver:

Kim J. Nonnegative matrix and tensor factorizations, least squares problems, and applications. [Internet] [Doctoral dissertation]. Georgia Tech; 2011. [cited 2019 Oct 20]. 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

URL: http://hdl.handle.net/1853/49091

► 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 (6^{th} 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 (16^{th} Edition):

Ouyang, Hua. “Optimal stochastic and distributed algorithms for machine learning.” 2013. Doctoral Dissertation, Georgia Tech. Accessed October 20, 2019. http://hdl.handle.net/1853/49091.

MLA Handbook (7^{th} Edition):

Ouyang, Hua. “Optimal stochastic and distributed algorithms for machine learning.” 2013. Web. 20 Oct 2019.

Vancouver:

Ouyang H. Optimal stochastic and distributed algorithms for machine learning. [Internet] [Doctoral dissertation]. Georgia Tech; 2013. [cited 2019 Oct 20]. 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

URL: http://hdl.handle.net/1853/50269

► 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 (6^{th} 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 (16^{th} Edition):

Riegel, Ryan Nelson. “Generalized N-body problems: a framework for scalable computation.” 2013. Doctoral Dissertation, Georgia Tech. Accessed October 20, 2019. http://hdl.handle.net/1853/50269.

MLA Handbook (7^{th} Edition):

Riegel, Ryan Nelson. “Generalized N-body problems: a framework for scalable computation.” 2013. Web. 20 Oct 2019.

Vancouver:

Riegel RN. Generalized N-body problems: a framework for scalable computation. [Internet] [Doctoral dissertation]. Georgia Tech; 2013. [cited 2019 Oct 20]. 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

URL: http://hdl.handle.net/1853/49121

► 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 (6^{th} 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 (16^{th} Edition):

Choo, Jae gul. “Integration of computational methods and visual analytics for large-scale high-dimensional data.” 2013. Doctoral Dissertation, Georgia Tech. Accessed October 20, 2019. http://hdl.handle.net/1853/49121.

MLA Handbook (7^{th} Edition):

Choo, Jae gul. “Integration of computational methods and visual analytics for large-scale high-dimensional data.” 2013. Web. 20 Oct 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 Oct 20]. 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

URL: http://hdl.handle.net/1853/45805

► 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 (6^{th} 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 (16^{th} Edition):

Crain, Steven P. “Personalized search and recommendation for health information resources.” 2012. Doctoral Dissertation, Georgia Tech. Accessed October 20, 2019. http://hdl.handle.net/1853/45805.

MLA Handbook (7^{th} Edition):

Crain, Steven P. “Personalized search and recommendation for health information resources.” 2012. Web. 20 Oct 2019.

Vancouver:

Crain SP. Personalized search and recommendation for health information resources. [Internet] [Doctoral dissertation]. Georgia Tech; 2012. [cited 2019 Oct 20]. 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. Bhat, Sooraj. Syntactic foundations for machine learning.

Degree: PhD, Computer Science, 2013, Georgia Tech

URL: http://hdl.handle.net/1853/47700

► 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 (6^{th} 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 (16^{th} Edition):

Bhat, Sooraj. “Syntactic foundations for machine learning.” 2013. Doctoral Dissertation, Georgia Tech. Accessed October 20, 2019. http://hdl.handle.net/1853/47700.

MLA Handbook (7^{th} Edition):

Bhat, Sooraj. “Syntactic foundations for machine learning.” 2013. Web. 20 Oct 2019.

Vancouver:

Bhat S. Syntactic foundations for machine learning. [Internet] [Doctoral dissertation]. Georgia Tech; 2013. [cited 2019 Oct 20]. 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

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

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

URL: http://hdl.handle.net/1853/49112

► 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 (6^{th} 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 (16^{th} Edition):

Ram, Parikshit. “New paradigms for approximate nearest-neighbor search.” 2013. Doctoral Dissertation, Georgia Tech. Accessed October 20, 2019. http://hdl.handle.net/1853/49112.

MLA Handbook (7^{th} Edition):

Ram, Parikshit. “New paradigms for approximate nearest-neighbor search.” 2013. Web. 20 Oct 2019.

Vancouver:

Ram P. New paradigms for approximate nearest-neighbor search. [Internet] [Doctoral dissertation]. Georgia Tech; 2013. [cited 2019 Oct 20]. 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

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

Degree: PhD, Computing, 2012, Georgia Tech

URL: http://hdl.handle.net/1853/45762

► 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 (6^{th} 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 (16^{th} Edition):

Baah, George Kofi. “Statistical causal analysis for fault localization.” 2012. Doctoral Dissertation, Georgia Tech. Accessed October 20, 2019. http://hdl.handle.net/1853/45762.

MLA Handbook (7^{th} Edition):

Baah, George Kofi. “Statistical causal analysis for fault localization.” 2012. Web. 20 Oct 2019.

Vancouver:

Baah GK. Statistical causal analysis for fault localization. [Internet] [Doctoral dissertation]. Georgia Tech; 2012. [cited 2019 Oct 20]. 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

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

Degree: PhD, Computing, 2012, Georgia Tech

URL: http://hdl.handle.net/1853/45793

► 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)

Record Details Similar Records

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

APA (6^{th} 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 (16^{th} Edition):

Sun, Mingxuan. “Visualizing and modeling partial incomplete ranking data.” 2012. Doctoral Dissertation, Georgia Tech. Accessed October 20, 2019. http://hdl.handle.net/1853/45793.

MLA Handbook (7^{th} Edition):

Sun, Mingxuan. “Visualizing and modeling partial incomplete ranking data.” 2012. Web. 20 Oct 2019.

Vancouver:

Sun M. Visualizing and modeling partial incomplete ranking data. [Internet] [Doctoral dissertation]. Georgia Tech; 2012. [cited 2019 Oct 20]. 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

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

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

URL: http://hdl.handle.net/1853/49116

► 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

Record Details Similar Records

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

APA (6^{th} 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 (16^{th} Edition):

March, William B. “Multi-tree algorithms for computational statistics and phyiscs.” 2013. Doctoral Dissertation, Georgia Tech. Accessed October 20, 2019. http://hdl.handle.net/1853/49116.

MLA Handbook (7^{th} Edition):

March, William B. “Multi-tree algorithms for computational statistics and phyiscs.” 2013. Web. 20 Oct 2019.

Vancouver:

March WB. Multi-tree algorithms for computational statistics and phyiscs. [Internet] [Doctoral dissertation]. Georgia Tech; 2013. [cited 2019 Oct 20]. 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

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

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

URL: http://hdl.handle.net/1853/53499

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

APA (6^{th} 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 (16^{th} Edition):

Tran, Long Quoc. “Efficient inference algorithms for network activities.” 2015. Doctoral Dissertation, Georgia Tech. Accessed October 20, 2019. http://hdl.handle.net/1853/53499.

MLA Handbook (7^{th} Edition):

Tran, Long Quoc. “Efficient inference algorithms for network activities.” 2015. Web. 20 Oct 2019.

Vancouver:

Tran LQ. Efficient inference algorithms for network activities. [Internet] [Doctoral dissertation]. Georgia Tech; 2015. [cited 2019 Oct 20]. 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

Georgia Tech

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

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

URL: http://hdl.handle.net/1853/19783

► 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

Record Details Similar Records

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

APA (6^{th} 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 (16^{th} Edition):

Lee, Teahyung. “Algorithm-Based Efficient Approaches for Motion Estimation Systems.” 2007. Doctoral Dissertation, Georgia Tech. Accessed October 20, 2019. http://hdl.handle.net/1853/19783.

MLA Handbook (7^{th} Edition):

Lee, Teahyung. “Algorithm-Based Efficient Approaches for Motion Estimation Systems.” 2007. Web. 20 Oct 2019.

Vancouver:

Lee T. Algorithm-Based Efficient Approaches for Motion Estimation Systems. [Internet] [Doctoral dissertation]. Georgia Tech; 2007. [cited 2019 Oct 20]. 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