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You searched for +publisher:"Georgia Tech" +contributor:("Isbell, Charles"). Showing records 1 – 30 of 35 total matches.

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Georgia Tech

1. Perez, Daniel Antonio. Performance comparison of support vector machine and relevance vector machine classifiers for functional MRI data.

Degree: MS, Biomedical Engineering, 2010, Georgia Tech

 Multivariate pattern analysis (MVPA) of fMRI data has been growing in popularity due to its sensitivity to networks of brain activation. It is performed in… (more)

Subjects/Keywords: Pattern recognition; Support vector machines; Relevance vector machines; Machine learning; FMRI; Diagnostic imaging; Magnetic resonance imaging; Imaging systems in medicine; Supervised learning (Machine learning)

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APA (6th Edition):

Perez, D. A. (2010). Performance comparison of support vector machine and relevance vector machine classifiers for functional MRI data. (Masters Thesis). Georgia Tech. Retrieved from http://hdl.handle.net/1853/34858

Chicago Manual of Style (16th Edition):

Perez, Daniel Antonio. “Performance comparison of support vector machine and relevance vector machine classifiers for functional MRI data.” 2010. Masters Thesis, Georgia Tech. Accessed September 16, 2019. http://hdl.handle.net/1853/34858.

MLA Handbook (7th Edition):

Perez, Daniel Antonio. “Performance comparison of support vector machine and relevance vector machine classifiers for functional MRI data.” 2010. Web. 16 Sep 2019.

Vancouver:

Perez DA. Performance comparison of support vector machine and relevance vector machine classifiers for functional MRI data. [Internet] [Masters thesis]. Georgia Tech; 2010. [cited 2019 Sep 16]. Available from: http://hdl.handle.net/1853/34858.

Council of Science Editors:

Perez DA. Performance comparison of support vector machine and relevance vector machine classifiers for functional MRI data. [Masters Thesis]. Georgia Tech; 2010. Available from: http://hdl.handle.net/1853/34858


Georgia Tech

2. 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 September 16, 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. 16 Sep 2019.

Vancouver:

Mehta NA. On sparse representations and new meta-learning paradigms for representation learning. [Internet] [Doctoral dissertation]. Georgia Tech; 2013. [cited 2019 Sep 16]. 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

3. Yu, Hong. A data-driven approach for personalized drama management.

Degree: PhD, Interactive Computing, 2015, Georgia Tech

 An interactive narrative is a form of digital entertainment in which players can create or influence a dramatic storyline through actions, typically by assuming the… (more)

Subjects/Keywords: Personalized drama manager; Interactive narrative; Player modeling; Prefix based collaborative filtering

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APA (6th Edition):

Yu, H. (2015). A data-driven approach for personalized drama management. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/53851

Chicago Manual of Style (16th Edition):

Yu, Hong. “A data-driven approach for personalized drama management.” 2015. Doctoral Dissertation, Georgia Tech. Accessed September 16, 2019. http://hdl.handle.net/1853/53851.

MLA Handbook (7th Edition):

Yu, Hong. “A data-driven approach for personalized drama management.” 2015. Web. 16 Sep 2019.

Vancouver:

Yu H. A data-driven approach for personalized drama management. [Internet] [Doctoral dissertation]. Georgia Tech; 2015. [cited 2019 Sep 16]. Available from: http://hdl.handle.net/1853/53851.

Council of Science Editors:

Yu H. A data-driven approach for personalized drama management. [Doctoral Dissertation]. Georgia Tech; 2015. Available from: http://hdl.handle.net/1853/53851


Georgia Tech

4. Nelson, Mark J. Representing and reasoning about videogame mechanics for automated design support.

Degree: PhD, Interactive Computing, 2015, Georgia Tech

 Videogame designers hope to sculpt gameplay, but actually work in the concrete medium of computation. What they create is code, artwork, dialogue – everything that goes… (more)

Subjects/Keywords: Design assistance; Game design; Logical modeling

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APA (6th Edition):

Nelson, M. J. (2015). Representing and reasoning about videogame mechanics for automated design support. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/53875

Chicago Manual of Style (16th Edition):

Nelson, Mark J. “Representing and reasoning about videogame mechanics for automated design support.” 2015. Doctoral Dissertation, Georgia Tech. Accessed September 16, 2019. http://hdl.handle.net/1853/53875.

MLA Handbook (7th Edition):

Nelson, Mark J. “Representing and reasoning about videogame mechanics for automated design support.” 2015. Web. 16 Sep 2019.

Vancouver:

Nelson MJ. Representing and reasoning about videogame mechanics for automated design support. [Internet] [Doctoral dissertation]. Georgia Tech; 2015. [cited 2019 Sep 16]. Available from: http://hdl.handle.net/1853/53875.

Council of Science Editors:

Nelson MJ. Representing and reasoning about videogame mechanics for automated design support. [Doctoral Dissertation]. Georgia Tech; 2015. Available from: http://hdl.handle.net/1853/53875


Georgia Tech

5. Berlind, Christopher. New insights on the power of active learning.

Degree: PhD, Computer Science, 2015, Georgia Tech

 Traditional supervised machine learning algorithms are expected to have access to a large corpus of labeled examples, but the massive amount of data available in… (more)

Subjects/Keywords: Machine learning; Learning theory; Active learning; Semi-supervised learning; Domain adaptation; Large margin learning

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APA (6th Edition):

Berlind, C. (2015). New insights on the power of active learning. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/53948

Chicago Manual of Style (16th Edition):

Berlind, Christopher. “New insights on the power of active learning.” 2015. Doctoral Dissertation, Georgia Tech. Accessed September 16, 2019. http://hdl.handle.net/1853/53948.

MLA Handbook (7th Edition):

Berlind, Christopher. “New insights on the power of active learning.” 2015. Web. 16 Sep 2019.

Vancouver:

Berlind C. New insights on the power of active learning. [Internet] [Doctoral dissertation]. Georgia Tech; 2015. [cited 2019 Sep 16]. Available from: http://hdl.handle.net/1853/53948.

Council of Science Editors:

Berlind C. New insights on the power of active learning. [Doctoral Dissertation]. Georgia Tech; 2015. Available from: http://hdl.handle.net/1853/53948


Georgia Tech

6. Kohlsdorf, Daniel. Data mining in large audio collections of dolphin signals.

Degree: PhD, Computer Science, 2015, Georgia Tech

 The study of dolphin cognition involves intensive research of animal vocal- izations recorded in the field. In this dissertation I address the automated analysis of… (more)

Subjects/Keywords: Pattern discovery; Dolphin communication

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APA (6th Edition):

Kohlsdorf, D. (2015). Data mining in large audio collections of dolphin signals. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/53968

Chicago Manual of Style (16th Edition):

Kohlsdorf, Daniel. “Data mining in large audio collections of dolphin signals.” 2015. Doctoral Dissertation, Georgia Tech. Accessed September 16, 2019. http://hdl.handle.net/1853/53968.

MLA Handbook (7th Edition):

Kohlsdorf, Daniel. “Data mining in large audio collections of dolphin signals.” 2015. Web. 16 Sep 2019.

Vancouver:

Kohlsdorf D. Data mining in large audio collections of dolphin signals. [Internet] [Doctoral dissertation]. Georgia Tech; 2015. [cited 2019 Sep 16]. Available from: http://hdl.handle.net/1853/53968.

Council of Science Editors:

Kohlsdorf D. Data mining in large audio collections of dolphin signals. [Doctoral Dissertation]. Georgia Tech; 2015. Available from: http://hdl.handle.net/1853/53968


Georgia Tech

7. Holmes, Michael P. Multi-tree Monte Carlo methods for fast, scalable machine learning.

Degree: PhD, Computing, 2009, Georgia Tech

 As modern applications of machine learning and data mining are forced to deal with ever more massive quantities of data, practitioners quickly run into difficulty… (more)

Subjects/Keywords: Machine learning; SVD; Scalable; Monte Carlo; Kernel estimators; Large data; Monte Carlo method; Trees Development Data processing; Algorithms; Computer algorithms

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APA (6th Edition):

Holmes, M. P. (2009). Multi-tree Monte Carlo methods for fast, scalable machine learning. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/33865

Chicago Manual of Style (16th Edition):

Holmes, Michael P. “Multi-tree Monte Carlo methods for fast, scalable machine learning.” 2009. Doctoral Dissertation, Georgia Tech. Accessed September 16, 2019. http://hdl.handle.net/1853/33865.

MLA Handbook (7th Edition):

Holmes, Michael P. “Multi-tree Monte Carlo methods for fast, scalable machine learning.” 2009. Web. 16 Sep 2019.

Vancouver:

Holmes MP. Multi-tree Monte Carlo methods for fast, scalable machine learning. [Internet] [Doctoral dissertation]. Georgia Tech; 2009. [cited 2019 Sep 16]. Available from: http://hdl.handle.net/1853/33865.

Council of Science Editors:

Holmes MP. Multi-tree Monte Carlo methods for fast, scalable machine learning. [Doctoral Dissertation]. Georgia Tech; 2009. Available from: http://hdl.handle.net/1853/33865


Georgia Tech

8. Roberts, David L. Computational techniques for reasoning about and shaping player experiences in interactive narratives.

Degree: PhD, Interactive Computing, 2010, Georgia Tech

 Interactive narratives are marked by two characteristics: 1) a space of player interactions, some subset of which are specified as aesthetic goals for the system;… (more)

Subjects/Keywords: Interactive storytelling; Drama management; Influence; Persuasion; Markov decision processes; Interactive multimedia; Shared virtual environments; Storytelling

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APA (6th Edition):

Roberts, D. L. (2010). Computational techniques for reasoning about and shaping player experiences in interactive narratives. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/33910

Chicago Manual of Style (16th Edition):

Roberts, David L. “Computational techniques for reasoning about and shaping player experiences in interactive narratives.” 2010. Doctoral Dissertation, Georgia Tech. Accessed September 16, 2019. http://hdl.handle.net/1853/33910.

MLA Handbook (7th Edition):

Roberts, David L. “Computational techniques for reasoning about and shaping player experiences in interactive narratives.” 2010. Web. 16 Sep 2019.

Vancouver:

Roberts DL. Computational techniques for reasoning about and shaping player experiences in interactive narratives. [Internet] [Doctoral dissertation]. Georgia Tech; 2010. [cited 2019 Sep 16]. Available from: http://hdl.handle.net/1853/33910.

Council of Science Editors:

Roberts DL. Computational techniques for reasoning about and shaping player experiences in interactive narratives. [Doctoral Dissertation]. Georgia Tech; 2010. Available from: http://hdl.handle.net/1853/33910


Georgia Tech

9. O'Flaherty, Rowland Wilde. A control theoretic perspective on learning in robotics.

Degree: PhD, Electrical and Computer Engineering, 2015, Georgia Tech

 For robotic systems to continue to move towards ubiquity, robots need to be more autonomous. More autonomy dictates that robots need to be able to… (more)

Subjects/Keywords: Learnability; Exploration vs exploitation; Ergodic; Optimal control

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APA (6th Edition):

O'Flaherty, R. W. (2015). A control theoretic perspective on learning in robotics. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/54833

Chicago Manual of Style (16th Edition):

O'Flaherty, Rowland Wilde. “A control theoretic perspective on learning in robotics.” 2015. Doctoral Dissertation, Georgia Tech. Accessed September 16, 2019. http://hdl.handle.net/1853/54833.

MLA Handbook (7th Edition):

O'Flaherty, Rowland Wilde. “A control theoretic perspective on learning in robotics.” 2015. Web. 16 Sep 2019.

Vancouver:

O'Flaherty RW. A control theoretic perspective on learning in robotics. [Internet] [Doctoral dissertation]. Georgia Tech; 2015. [cited 2019 Sep 16]. Available from: http://hdl.handle.net/1853/54833.

Council of Science Editors:

O'Flaherty RW. A control theoretic perspective on learning in robotics. [Doctoral Dissertation]. Georgia Tech; 2015. Available from: http://hdl.handle.net/1853/54833


Georgia Tech

10. Krening, Samantha. Humans Teaching Intelligent Agents with Verbal Instruction.

Degree: PhD, Aerospace Engineering, 2019, Georgia Tech

 The widespread integration of robotics into everyday life requires significant improvement in the underlying machine learning (ML) agents to make them more accessible, customizable, and… (more)

Subjects/Keywords: Robotics; Machine Learning; Interactive Machine Learning; Human-Agent Interaction; Reinforcement Learning; Natural Language Processing; Human-Computer Interaction; Human Factors; Machine Learning Verification

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APA (6th Edition):

Krening, S. (2019). Humans Teaching Intelligent Agents with Verbal Instruction. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/61232

Chicago Manual of Style (16th Edition):

Krening, Samantha. “Humans Teaching Intelligent Agents with Verbal Instruction.” 2019. Doctoral Dissertation, Georgia Tech. Accessed September 16, 2019. http://hdl.handle.net/1853/61232.

MLA Handbook (7th Edition):

Krening, Samantha. “Humans Teaching Intelligent Agents with Verbal Instruction.” 2019. Web. 16 Sep 2019.

Vancouver:

Krening S. Humans Teaching Intelligent Agents with Verbal Instruction. [Internet] [Doctoral dissertation]. Georgia Tech; 2019. [cited 2019 Sep 16]. Available from: http://hdl.handle.net/1853/61232.

Council of Science Editors:

Krening S. Humans Teaching Intelligent Agents with Verbal Instruction. [Doctoral Dissertation]. Georgia Tech; 2019. Available from: http://hdl.handle.net/1853/61232


Georgia Tech

11. Edwards, Ashley Deloris. Emulation and imitation via perceptual goal specifications.

Degree: PhD, Computer Science, 2019, Georgia Tech

 This dissertation aims to demonstrate how perceptual goal specifications may be used as alternative representations for specifying domain-specific reward functions for reinforcement learning. The works… (more)

Subjects/Keywords: Reinforcement learning; goal specification; imitation learning; reward design

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APA (6th Edition):

Edwards, A. D. (2019). Emulation and imitation via perceptual goal specifications. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/61234

Chicago Manual of Style (16th Edition):

Edwards, Ashley Deloris. “Emulation and imitation via perceptual goal specifications.” 2019. Doctoral Dissertation, Georgia Tech. Accessed September 16, 2019. http://hdl.handle.net/1853/61234.

MLA Handbook (7th Edition):

Edwards, Ashley Deloris. “Emulation and imitation via perceptual goal specifications.” 2019. Web. 16 Sep 2019.

Vancouver:

Edwards AD. Emulation and imitation via perceptual goal specifications. [Internet] [Doctoral dissertation]. Georgia Tech; 2019. [cited 2019 Sep 16]. Available from: http://hdl.handle.net/1853/61234.

Council of Science Editors:

Edwards AD. Emulation and imitation via perceptual goal specifications. [Doctoral Dissertation]. Georgia Tech; 2019. Available from: http://hdl.handle.net/1853/61234


Georgia Tech

12. Sawhney, Rahul. Robust approaches and optimization for 3D data.

Degree: PhD, Interactive Computing, 2018, Georgia Tech

 We introduce a robust, purely geometric, representation framework for fundamental association and analysis problems involving multiple views and scenes. The framework utilizes surface patches /… (more)

Subjects/Keywords: 3D; Geometry; Robust; Optimization; Association; Retrieval; Robust Loss; Viewpoint invariance; RGB-D; Depth image; Point cloud; Geometric description; Matching; Correspondence; Registration; Reconstruction; Surface; Patch; Segment; Superpixel; Majorization minorization; Outlier rejection; Model fitting; Estimation; Nonlinear Least Absolute Deviations; Mean shift; Mode seeking; Segmentation; Hierarchical; Geometric diversity; Nonsmooth; Nonconvex; Edit distance; Damerau Levenshtein; Proximal algorithms; Determinantal Point Process; Fisher Vector; Feature Space; Variational Factorization; M - Estimation; Structured Estimation

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APA (6th Edition):

Sawhney, R. (2018). Robust approaches and optimization for 3D data. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/61103

Chicago Manual of Style (16th Edition):

Sawhney, Rahul. “Robust approaches and optimization for 3D data.” 2018. Doctoral Dissertation, Georgia Tech. Accessed September 16, 2019. http://hdl.handle.net/1853/61103.

MLA Handbook (7th Edition):

Sawhney, Rahul. “Robust approaches and optimization for 3D data.” 2018. Web. 16 Sep 2019.

Vancouver:

Sawhney R. Robust approaches and optimization for 3D data. [Internet] [Doctoral dissertation]. Georgia Tech; 2018. [cited 2019 Sep 16]. Available from: http://hdl.handle.net/1853/61103.

Council of Science Editors:

Sawhney R. Robust approaches and optimization for 3D data. [Doctoral Dissertation]. Georgia Tech; 2018. Available from: http://hdl.handle.net/1853/61103


Georgia Tech

13. Liang, Yingyu. Modern aspects of unsupervised learning.

Degree: PhD, Computer Science, 2014, Georgia Tech

 Unsupervised learning has become more and more important due to the recent explosion of data. Clustering, a key topic in unsupervised learning, is a well-studied… (more)

Subjects/Keywords: Unsupervised learning; Clustering; Perturbation resilience; Distributed clustering; Community detection

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APA (6th Edition):

Liang, Y. (2014). Modern aspects of unsupervised learning. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/52282

Chicago Manual of Style (16th Edition):

Liang, Yingyu. “Modern aspects of unsupervised learning.” 2014. Doctoral Dissertation, Georgia Tech. Accessed September 16, 2019. http://hdl.handle.net/1853/52282.

MLA Handbook (7th Edition):

Liang, Yingyu. “Modern aspects of unsupervised learning.” 2014. Web. 16 Sep 2019.

Vancouver:

Liang Y. Modern aspects of unsupervised learning. [Internet] [Doctoral dissertation]. Georgia Tech; 2014. [cited 2019 Sep 16]. Available from: http://hdl.handle.net/1853/52282.

Council of Science Editors:

Liang Y. Modern aspects of unsupervised learning. [Doctoral Dissertation]. Georgia Tech; 2014. Available from: http://hdl.handle.net/1853/52282


Georgia Tech

14. Guzdial, Matthew James. Combinational Machine Learning Creativity.

Degree: PhD, Interactive Computing, 2019, Georgia Tech

 Computational creativity is a field focused on the study and development of behaviors in computers an observer would deem creative. Traditionally, it has relied upon… (more)

Subjects/Keywords: Machine learning; Computational creativity' Game AI; Artificial Intelligence; Procedural content generation; Combinational creativity

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APA (6th Edition):

Guzdial, M. J. (2019). Combinational Machine Learning Creativity. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/61790

Chicago Manual of Style (16th Edition):

Guzdial, Matthew James. “Combinational Machine Learning Creativity.” 2019. Doctoral Dissertation, Georgia Tech. Accessed September 16, 2019. http://hdl.handle.net/1853/61790.

MLA Handbook (7th Edition):

Guzdial, Matthew James. “Combinational Machine Learning Creativity.” 2019. Web. 16 Sep 2019.

Vancouver:

Guzdial MJ. Combinational Machine Learning Creativity. [Internet] [Doctoral dissertation]. Georgia Tech; 2019. [cited 2019 Sep 16]. Available from: http://hdl.handle.net/1853/61790.

Council of Science Editors:

Guzdial MJ. Combinational Machine Learning Creativity. [Doctoral Dissertation]. Georgia Tech; 2019. Available from: http://hdl.handle.net/1853/61790

15. Marquez, Nicholas Alexander. OOCFA2: a PDA-based higher-order flow analysis for object-oriented programs.

Degree: MS, Computer Science, 2013, Georgia Tech

 The application of higher-order PDA-based flow analyses to object-oriented languages enables comprehensive and precise characterization of program behavior, while retaining practicality with efficiency. We implement… (more)

Subjects/Keywords: CFA2; kCFA; Dalvik; Java; JVM; Securty analysis; Static analysis; Object-oriented programming (Computer science); Object-oriented programming languages; Operating systems (Computers); Data protection

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APA (6th Edition):

Marquez, N. A. (2013). OOCFA2: a PDA-based higher-order flow analysis for object-oriented programs. (Masters Thesis). Georgia Tech. Retrieved from http://hdl.handle.net/1853/47556

Chicago Manual of Style (16th Edition):

Marquez, Nicholas Alexander. “OOCFA2: a PDA-based higher-order flow analysis for object-oriented programs.” 2013. Masters Thesis, Georgia Tech. Accessed September 16, 2019. http://hdl.handle.net/1853/47556.

MLA Handbook (7th Edition):

Marquez, Nicholas Alexander. “OOCFA2: a PDA-based higher-order flow analysis for object-oriented programs.” 2013. Web. 16 Sep 2019.

Vancouver:

Marquez NA. OOCFA2: a PDA-based higher-order flow analysis for object-oriented programs. [Internet] [Masters thesis]. Georgia Tech; 2013. [cited 2019 Sep 16]. Available from: http://hdl.handle.net/1853/47556.

Council of Science Editors:

Marquez NA. OOCFA2: a PDA-based higher-order flow analysis for object-oriented programs. [Masters Thesis]. Georgia Tech; 2013. Available from: http://hdl.handle.net/1853/47556

16. Scholz, Jonathan. Physics-based reinforcement learning for autonomous manipulation.

Degree: PhD, Interactive Computing, 2015, Georgia Tech

 With recent research advances, the dream of bringing domestic robots into our everyday lives has become more plausible than ever. Domestic robotics has grown dramatically… (more)

Subjects/Keywords: Machine learning; Robotics; Reinforcement learning

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APA (6th Edition):

Scholz, J. (2015). Physics-based reinforcement learning for autonomous manipulation. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/54366

Chicago Manual of Style (16th Edition):

Scholz, Jonathan. “Physics-based reinforcement learning for autonomous manipulation.” 2015. Doctoral Dissertation, Georgia Tech. Accessed September 16, 2019. http://hdl.handle.net/1853/54366.

MLA Handbook (7th Edition):

Scholz, Jonathan. “Physics-based reinforcement learning for autonomous manipulation.” 2015. Web. 16 Sep 2019.

Vancouver:

Scholz J. Physics-based reinforcement learning for autonomous manipulation. [Internet] [Doctoral dissertation]. Georgia Tech; 2015. [cited 2019 Sep 16]. Available from: http://hdl.handle.net/1853/54366.

Council of Science Editors:

Scholz J. Physics-based reinforcement learning for autonomous manipulation. [Doctoral Dissertation]. Georgia Tech; 2015. Available from: http://hdl.handle.net/1853/54366

17. Kira, Zsolt. Communication and alignment of grounded symbolic knowledge among heterogeneous robots.

Degree: PhD, Computing, 2010, Georgia Tech

 Experience forms the basis of learning. It is crucial in the development of human intelligence, and more broadly allows an agent to discover and learn… (more)

Subjects/Keywords: Heterogeneous robot teams; Robot transfer learning; Grounded concept learning; Cognitive Vision; Robot perception; Conceptual spaces; Machine learning; Robotics; Multiagent systems

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APA (6th Edition):

Kira, Z. (2010). Communication and alignment of grounded symbolic knowledge among heterogeneous robots. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/33941

Chicago Manual of Style (16th Edition):

Kira, Zsolt. “Communication and alignment of grounded symbolic knowledge among heterogeneous robots.” 2010. Doctoral Dissertation, Georgia Tech. Accessed September 16, 2019. http://hdl.handle.net/1853/33941.

MLA Handbook (7th Edition):

Kira, Zsolt. “Communication and alignment of grounded symbolic knowledge among heterogeneous robots.” 2010. Web. 16 Sep 2019.

Vancouver:

Kira Z. Communication and alignment of grounded symbolic knowledge among heterogeneous robots. [Internet] [Doctoral dissertation]. Georgia Tech; 2010. [cited 2019 Sep 16]. Available from: http://hdl.handle.net/1853/33941.

Council of Science Editors:

Kira Z. Communication and alignment of grounded symbolic knowledge among heterogeneous robots. [Doctoral Dissertation]. Georgia Tech; 2010. Available from: http://hdl.handle.net/1853/33941

18. 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 September 16, 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. 16 Sep 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 Sep 16]. 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

19. Ashbrook, Daniel Lee. Enabling mobile microinteractions.

Degree: PhD, Computing, 2010, Georgia Tech

 While much attention has been paid to the usability of desktop computers, mobile com- puters are quickly becoming the dominant platform. Because mobile computers may… (more)

Subjects/Keywords: Wristwatch interfaces; Human-computer interaction; Wearable computing; Mobile computing; Touch screens; Gesture; User interfaces (Computer systems); Wearable computers

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APA (6th Edition):

Ashbrook, D. L. (2010). Enabling mobile microinteractions. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/33986

Chicago Manual of Style (16th Edition):

Ashbrook, Daniel Lee. “Enabling mobile microinteractions.” 2010. Doctoral Dissertation, Georgia Tech. Accessed September 16, 2019. http://hdl.handle.net/1853/33986.

MLA Handbook (7th Edition):

Ashbrook, Daniel Lee. “Enabling mobile microinteractions.” 2010. Web. 16 Sep 2019.

Vancouver:

Ashbrook DL. Enabling mobile microinteractions. [Internet] [Doctoral dissertation]. Georgia Tech; 2010. [cited 2019 Sep 16]. Available from: http://hdl.handle.net/1853/33986.

Council of Science Editors:

Ashbrook DL. Enabling mobile microinteractions. [Doctoral Dissertation]. Georgia Tech; 2010. Available from: http://hdl.handle.net/1853/33986

20. 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 September 16, 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. 16 Sep 2019.

Vancouver:

Riegel RN. Generalized N-body problems: a framework for scalable computation. [Internet] [Doctoral dissertation]. Georgia Tech; 2013. [cited 2019 Sep 16]. 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

21. Feldman, Adam Michael. Using observations to recognize the behavior of interacting multi-agent systems.

Degree: PhD, Computing, 2008, Georgia Tech

 Behavioral research involves the study of the behaviors of one or more agents (often animals) in order to better understand the agents' thoughts and actions.… (more)

Subjects/Keywords: Behavior recognition; Multi-target tracking; Behavioral assessment; Automatic data collection systems; Automatic tracking

…names, but you know who you are. Thanks also must go to my friends, both at Georgia Tech and… 

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APA (6th Edition):

Feldman, A. M. (2008). Using observations to recognize the behavior of interacting multi-agent systems. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/24771

Chicago Manual of Style (16th Edition):

Feldman, Adam Michael. “Using observations to recognize the behavior of interacting multi-agent systems.” 2008. Doctoral Dissertation, Georgia Tech. Accessed September 16, 2019. http://hdl.handle.net/1853/24771.

MLA Handbook (7th Edition):

Feldman, Adam Michael. “Using observations to recognize the behavior of interacting multi-agent systems.” 2008. Web. 16 Sep 2019.

Vancouver:

Feldman AM. Using observations to recognize the behavior of interacting multi-agent systems. [Internet] [Doctoral dissertation]. Georgia Tech; 2008. [cited 2019 Sep 16]. Available from: http://hdl.handle.net/1853/24771.

Council of Science Editors:

Feldman AM. Using observations to recognize the behavior of interacting multi-agent systems. [Doctoral Dissertation]. Georgia Tech; 2008. Available from: http://hdl.handle.net/1853/24771

22. Simpkins, Christopher Lee. Integrating reinforcement learning into a programming language.

Degree: PhD, Computer Science, 2017, Georgia Tech

 Reinforcement learning is a promising solution to the intelligent agent problem, namely, given the state of the world, which action should an agent take to… (more)

Subjects/Keywords: Machine learning; Reinforcement learning; Modular reinforcement learning; Programming languages; Domain specific languages; Software engineering; Artificial intelligence; Intelligent agents

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APA (6th Edition):

Simpkins, C. L. (2017). Integrating reinforcement learning into a programming language. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/58683

Chicago Manual of Style (16th Edition):

Simpkins, Christopher Lee. “Integrating reinforcement learning into a programming language.” 2017. Doctoral Dissertation, Georgia Tech. Accessed September 16, 2019. http://hdl.handle.net/1853/58683.

MLA Handbook (7th Edition):

Simpkins, Christopher Lee. “Integrating reinforcement learning into a programming language.” 2017. Web. 16 Sep 2019.

Vancouver:

Simpkins CL. Integrating reinforcement learning into a programming language. [Internet] [Doctoral dissertation]. Georgia Tech; 2017. [cited 2019 Sep 16]. Available from: http://hdl.handle.net/1853/58683.

Council of Science Editors:

Simpkins CL. Integrating reinforcement learning into a programming language. [Doctoral Dissertation]. Georgia Tech; 2017. Available from: http://hdl.handle.net/1853/58683

23. Curtin, Ryan Ross. Improving dual-tree algorithms.

Degree: PhD, Electrical and Computer Engineering, 2015, Georgia Tech

 This large body of work is entirely centered around dual-tree algorithms, a class of algorithm based on spatial indexing structures that often provide large amounts… (more)

Subjects/Keywords: Machine learning; Computational geometry; Tree-based algorithms; Dual-tree algorithms; Kd-tree; Nearest neighbor search; K-means clustering; Data mining

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APA (6th Edition):

Curtin, R. R. (2015). Improving dual-tree algorithms. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/54354

Chicago Manual of Style (16th Edition):

Curtin, Ryan Ross. “Improving dual-tree algorithms.” 2015. Doctoral Dissertation, Georgia Tech. Accessed September 16, 2019. http://hdl.handle.net/1853/54354.

MLA Handbook (7th Edition):

Curtin, Ryan Ross. “Improving dual-tree algorithms.” 2015. Web. 16 Sep 2019.

Vancouver:

Curtin RR. Improving dual-tree algorithms. [Internet] [Doctoral dissertation]. Georgia Tech; 2015. [cited 2019 Sep 16]. Available from: http://hdl.handle.net/1853/54354.

Council of Science Editors:

Curtin RR. Improving dual-tree algorithms. [Doctoral Dissertation]. Georgia Tech; 2015. Available from: http://hdl.handle.net/1853/54354

24. Akgun, Baris. Robots learning actions and goals from everyday people.

Degree: PhD, Interactive Computing, 2015, Georgia Tech

 Robots are destined to move beyond the caged factory floors towards domains where they will be interacting closely with humans. They will encounter highly varied… (more)

Subjects/Keywords: Robotics; Self-learning; Social-learning; Learning from demonstration; Human robot interaction

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APA (6th Edition):

Akgun, B. (2015). Robots learning actions and goals from everyday people. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/54435

Chicago Manual of Style (16th Edition):

Akgun, Baris. “Robots learning actions and goals from everyday people.” 2015. Doctoral Dissertation, Georgia Tech. Accessed September 16, 2019. http://hdl.handle.net/1853/54435.

MLA Handbook (7th Edition):

Akgun, Baris. “Robots learning actions and goals from everyday people.” 2015. Web. 16 Sep 2019.

Vancouver:

Akgun B. Robots learning actions and goals from everyday people. [Internet] [Doctoral dissertation]. Georgia Tech; 2015. [cited 2019 Sep 16]. Available from: http://hdl.handle.net/1853/54435.

Council of Science Editors:

Akgun B. Robots learning actions and goals from everyday people. [Doctoral Dissertation]. Georgia Tech; 2015. Available from: http://hdl.handle.net/1853/54435

25. Zook, Alexander. Automated iterative game design.

Degree: PhD, Interactive Computing, 2016, Georgia Tech

 Computational systems to model aspects of iterative game design were proposed, encompassing: game generation, sampling behaviors in a game, analyzing game behaviors for patterns, and… (more)

Subjects/Keywords: Artificial intelligence; Machine learning; Game design; Computational creativity; Games

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APA (6th Edition):

Zook, A. (2016). Automated iterative game design. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/56346

Chicago Manual of Style (16th Edition):

Zook, Alexander. “Automated iterative game design.” 2016. Doctoral Dissertation, Georgia Tech. Accessed September 16, 2019. http://hdl.handle.net/1853/56346.

MLA Handbook (7th Edition):

Zook, Alexander. “Automated iterative game design.” 2016. Web. 16 Sep 2019.

Vancouver:

Zook A. Automated iterative game design. [Internet] [Doctoral dissertation]. Georgia Tech; 2016. [cited 2019 Sep 16]. Available from: http://hdl.handle.net/1853/56346.

Council of Science Editors:

Zook A. Automated iterative game design. [Doctoral Dissertation]. Georgia Tech; 2016. Available from: http://hdl.handle.net/1853/56346

26. Levihn, Martin. Autonomous environment manipulation to facilitate task completion.

Degree: PhD, Interactive Computing, 2015, Georgia Tech

 A robot should be able to autonomously modify and utilize its environment to assist its task completion. While mobile manipulators and humanoid robots have both… (more)

Subjects/Keywords: Navigation Using Movable Obstacles; Robot object use; Navigation using movable obstacles

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APA (6th Edition):

Levihn, M. (2015). Autonomous environment manipulation to facilitate task completion. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/53543

Chicago Manual of Style (16th Edition):

Levihn, Martin. “Autonomous environment manipulation to facilitate task completion.” 2015. Doctoral Dissertation, Georgia Tech. Accessed September 16, 2019. http://hdl.handle.net/1853/53543.

MLA Handbook (7th Edition):

Levihn, Martin. “Autonomous environment manipulation to facilitate task completion.” 2015. Web. 16 Sep 2019.

Vancouver:

Levihn M. Autonomous environment manipulation to facilitate task completion. [Internet] [Doctoral dissertation]. Georgia Tech; 2015. [cited 2019 Sep 16]. Available from: http://hdl.handle.net/1853/53543.

Council of Science Editors:

Levihn M. Autonomous environment manipulation to facilitate task completion. [Doctoral Dissertation]. Georgia Tech; 2015. Available from: http://hdl.handle.net/1853/53543

27. Irani, Arya John. Utilizing negative policy information to accelerate reinforcement learning.

Degree: PhD, Interactive Computing, 2015, Georgia Tech

 A pilot study by Subramanian et al. on Markov decision problem task decomposition by humans revealed that participants break down tasks into both short-term subgoals… (more)

Subjects/Keywords:

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APA (6th Edition):

Irani, A. J. (2015). Utilizing negative policy information to accelerate reinforcement learning. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/53481

Chicago Manual of Style (16th Edition):

Irani, Arya John. “Utilizing negative policy information to accelerate reinforcement learning.” 2015. Doctoral Dissertation, Georgia Tech. Accessed September 16, 2019. http://hdl.handle.net/1853/53481.

MLA Handbook (7th Edition):

Irani, Arya John. “Utilizing negative policy information to accelerate reinforcement learning.” 2015. Web. 16 Sep 2019.

Vancouver:

Irani AJ. Utilizing negative policy information to accelerate reinforcement learning. [Internet] [Doctoral dissertation]. Georgia Tech; 2015. [cited 2019 Sep 16]. Available from: http://hdl.handle.net/1853/53481.

Council of Science Editors:

Irani AJ. Utilizing negative policy information to accelerate reinforcement learning. [Doctoral Dissertation]. Georgia Tech; 2015. Available from: http://hdl.handle.net/1853/53481

28. Lewi, Jeremy. Sequential optimal design of neurophysiology experiments.

Degree: PhD, Biomedical Engineering, 2009, Georgia Tech

 For well over 200 years, scientists and doctors have been poking and prodding brains in every which way in an effort to understand how they… (more)

Subjects/Keywords: Generalized linear model (GLM); Active learning; Sequential optimal experimental design; Neurophysiology; Experimental design; Combinatorial optimization

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APA (6th Edition):

Lewi, J. (2009). Sequential optimal design of neurophysiology experiments. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/28201

Chicago Manual of Style (16th Edition):

Lewi, Jeremy. “Sequential optimal design of neurophysiology experiments.” 2009. Doctoral Dissertation, Georgia Tech. Accessed September 16, 2019. http://hdl.handle.net/1853/28201.

MLA Handbook (7th Edition):

Lewi, Jeremy. “Sequential optimal design of neurophysiology experiments.” 2009. Web. 16 Sep 2019.

Vancouver:

Lewi J. Sequential optimal design of neurophysiology experiments. [Internet] [Doctoral dissertation]. Georgia Tech; 2009. [cited 2019 Sep 16]. Available from: http://hdl.handle.net/1853/28201.

Council of Science Editors:

Lewi J. Sequential optimal design of neurophysiology experiments. [Doctoral Dissertation]. Georgia Tech; 2009. Available from: http://hdl.handle.net/1853/28201

29. Otero, Richard Edward. Problem decomposition by mutual information and force-based clustering.

Degree: PhD, Aerospace Engineering, 2012, Georgia Tech

 The scale of engineering problems has sharply increased over the last twenty years. Larger coupled systems, increasing complexity, and limited resources create a need for… (more)

Subjects/Keywords: DSM; Low thrust; Decomposition; MIMIC; GA; Global optimization; Mutual information; Force based; Importance metric; Trajectory design; Multidisciplinary design optimization; Engineering design

…3rd Global Trajectory Optimization Competition Entry by Georgia Tech. 84 46 Trajectory Leg… 

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APA (6th Edition):

Otero, R. E. (2012). Problem decomposition by mutual information and force-based clustering. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/43641

Chicago Manual of Style (16th Edition):

Otero, Richard Edward. “Problem decomposition by mutual information and force-based clustering.” 2012. Doctoral Dissertation, Georgia Tech. Accessed September 16, 2019. http://hdl.handle.net/1853/43641.

MLA Handbook (7th Edition):

Otero, Richard Edward. “Problem decomposition by mutual information and force-based clustering.” 2012. Web. 16 Sep 2019.

Vancouver:

Otero RE. Problem decomposition by mutual information and force-based clustering. [Internet] [Doctoral dissertation]. Georgia Tech; 2012. [cited 2019 Sep 16]. Available from: http://hdl.handle.net/1853/43641.

Council of Science Editors:

Otero RE. Problem decomposition by mutual information and force-based clustering. [Doctoral Dissertation]. Georgia Tech; 2012. Available from: http://hdl.handle.net/1853/43641

30. 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 September 16, 2019. http://hdl.handle.net/1853/47700.

MLA Handbook (7th Edition):

Bhat, Sooraj. “Syntactic foundations for machine learning.” 2013. Web. 16 Sep 2019.

Vancouver:

Bhat S. Syntactic foundations for machine learning. [Internet] [Doctoral dissertation]. Georgia Tech; 2013. [cited 2019 Sep 16]. 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

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