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Colorado State University
1.
Urbanska, Malgorzata.
Automated security analysis of the home computer.
Degree: MS(M.S.), Computer Science, 2014, Colorado State University
URL: http://hdl.handle.net/10217/82538 
► Home computer users pose special challenges to the security of their machines. Often home computer users do not realize that their computer activities have repercussions…
(more)
▼ Home computer users pose special challenges to the security of their machines. Often home computer users do not realize that their computer activities have repercussions on computer security. Frequently, they are not aware about their role in keeping their home computer secure. Therefore, security analysis solutions for a home computer must differ significantly from standard security solutions. In addition to considering the properties of a single system, the characteristics of a home user have to be deliberated. Attack Graphs (AGs) are models that have been widely used for security analysis. A Personalized Attack Graph (PAG) extends the traditional AGs for this purpose. It characterizes the interplay between vulnerabilities, user actions, attacker strategies, and system activities. Success of such security analysis depends on the level of detailed information used to build the PAG. Because the PAG can have hundreds of elements and manual analysis can be error-prone and tedious, automation of this process is an essential component in the security analysis for the home computer user. Automated security analysis, which applies the PAG, requires information about user behavior, attacker and system actions, and vulnerabilities that are present in the home computer. In this thesis, we expatiate on 1) modeling home user behavior in order to obtain user specific information, 2) analyzing vulnerability information resources to get the most detailed vulnerability descriptions, and 3) transforming vulnerability information into a format useful for automated construction of the PAG. We propose the Bayesian User Action model that quantitatively represents the relationships between different user characteristics and provides the likelihood of a user taking a specific cyber related action. This model complements the PAG by delivering information about the home user. We demonstrate how different user behavior affects exploit likelihood in the PAG. We compare different vulnerability information sources in order to identify the best source for security analysis of the home computer. We calculate contextual similarity of the vulnerability descriptions to identify the same vulnerabilities from different vulnerability databases. We measure the similarity of vulnerability descriptions of the same vulnerability from multiple sources in order to identify any additional information that can be used to construct the PAG. We demonstrate a methodology of transforming a textual vulnerability description into a more structured format. We use Information Extraction (IE) techniques that are based on regular expression rules and dictionaries of keywords. We extract five types of information: infected software, attacker/user/system preconditions, and postconditions of exploiting vulnerabilities. We evaluate the performance of our IE system by measuring accuracy for each type of extracted information. Experiments on influence of user profile on the PAG show that probability of exploits differ depending on user personality. Results also…
Advisors/Committee Members: Ray, Indrajit (advisor), Howe, Adele E. (advisor), Byrne, Zinta (committee member).
Subjects/Keywords: attacks and defenses; attack graphs; security personalization; security risk modeling; system security; vulnerability database
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APA (6th Edition):
Urbanska, M. (2014). Automated security analysis of the home computer. (Masters Thesis). Colorado State University. Retrieved from http://hdl.handle.net/10217/82538
Chicago Manual of Style (16th Edition):
Urbanska, Malgorzata. “Automated security analysis of the home computer.” 2014. Masters Thesis, Colorado State University. Accessed April 21, 2021.
http://hdl.handle.net/10217/82538.
MLA Handbook (7th Edition):
Urbanska, Malgorzata. “Automated security analysis of the home computer.” 2014. Web. 21 Apr 2021.
Vancouver:
Urbanska M. Automated security analysis of the home computer. [Internet] [Masters thesis]. Colorado State University; 2014. [cited 2021 Apr 21].
Available from: http://hdl.handle.net/10217/82538.
Council of Science Editors:
Urbanska M. Automated security analysis of the home computer. [Masters Thesis]. Colorado State University; 2014. Available from: http://hdl.handle.net/10217/82538
Colorado State University
2.
Jonardi, Eric.
Hierarchical framework for energy-efficient resource management in green data centers, A.
Degree: MS(M.S.), Electrical and Computer Engineering, 2015, Colorado State University
URL: http://hdl.handle.net/10217/167188
► Data centers and high performance computing systems are increasing in both size and number. The massive electricity consumption of these systems results in huge electricity…
(more)
▼ Data centers and high performance computing systems are increasing in both size and number. The massive electricity consumption of these systems results in huge electricity costs, a trend that will become commercially unsustainable as systems grow even larger. Optimizations to improve energy-efficiency and reduce electricity costs can be implemented at multiple system levels, and are explored in this thesis at the server node, data center, and geo-distributed data center levels. Frameworks are proposed for each level to improve energy-efficiency and reduce electricity costs. As the core count in processors continues to rise, applications are increasingly experiencing performance degradation due to co-location interference arising from contention for shared resources. The first part of this thesis proposes a methodology for modeling these co-location interference effects to enable accurate predictions of execution time for co-located applications, reducing or even eliminating the need to over-provision server resources to meet quality of service requirements, and improving overall system efficiency. In the second part of this thesis a thermal-, power-, and machine-heterogeneity-aware resource allocation framework is proposed for a single data center to reduce both total server power and the power required to cool the data center, while maximizing the reward of the executed workload in over-subscribed scenarios. The final part of this thesis explores the optimization of geo-distributed data centers, which are growing in number with the rise of cloud computing. A geographical load balancing framework with time-of-use pricing and integrated renewable power is designed, and it is demonstrated how increasing the detail of system knowledge and considering all system levels simultaneously can significantly improve electricity cost savings for geo-distributed systems.
Advisors/Committee Members: Pasricha, Sudeep (advisor), Siegel, H. J. (advisor), Howe, Adele (committee member).
Subjects/Keywords: energy efficiency; high-performance computing; resource allocation; heterogeneous computing; co-location; optimization
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APA (6th Edition):
Jonardi, E. (2015). Hierarchical framework for energy-efficient resource management in green data centers, A. (Masters Thesis). Colorado State University. Retrieved from http://hdl.handle.net/10217/167188
Chicago Manual of Style (16th Edition):
Jonardi, Eric. “Hierarchical framework for energy-efficient resource management in green data centers, A.” 2015. Masters Thesis, Colorado State University. Accessed April 21, 2021.
http://hdl.handle.net/10217/167188.
MLA Handbook (7th Edition):
Jonardi, Eric. “Hierarchical framework for energy-efficient resource management in green data centers, A.” 2015. Web. 21 Apr 2021.
Vancouver:
Jonardi E. Hierarchical framework for energy-efficient resource management in green data centers, A. [Internet] [Masters thesis]. Colorado State University; 2015. [cited 2021 Apr 21].
Available from: http://hdl.handle.net/10217/167188.
Council of Science Editors:
Jonardi E. Hierarchical framework for energy-efficient resource management in green data centers, A. [Masters Thesis]. Colorado State University; 2015. Available from: http://hdl.handle.net/10217/167188
Colorado State University
3.
Adams, Laura.
Enzyme selection for optical mapping is hard.
Degree: MS(M.S.), Computer Science, 2015, Colorado State University
URL: http://hdl.handle.net/10217/167113
► The process of assembling a genome, without access to a reference genome, is prone to a type of error called a misassembly error. These errors…
(more)
▼ The process of assembling a genome, without access to a reference genome, is prone to a type of error called a misassembly error. These errors are difficult to detect and can mimic true, biological variation. Optical mapping data has been shown to have the potential to reduce misassembly errors in draft genomes. Optical mapping data is generated using digestion enzymes on a genome. In this paper, we formulate the problem of selecting optimal digestion enzymes to create the most informative optical map. We show this process in NP-hard and W[1]-hard. We also propose and evaluate a machine learning method using a support vector machine and feature reduction to estimate the optimal enzymes. Using this method, we were able to predict two optimal enzymes exactly and estimate three more within reasonable similarity.
Advisors/Committee Members: Boucher, Christina (advisor), Howe, Adele (committee member), Ingram, Patrick (committee member).
Subjects/Keywords: genome assembly; optical mapping; misassembly error; enzyme selection
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APA (6th Edition):
Adams, L. (2015). Enzyme selection for optical mapping is hard. (Masters Thesis). Colorado State University. Retrieved from http://hdl.handle.net/10217/167113
Chicago Manual of Style (16th Edition):
Adams, Laura. “Enzyme selection for optical mapping is hard.” 2015. Masters Thesis, Colorado State University. Accessed April 21, 2021.
http://hdl.handle.net/10217/167113.
MLA Handbook (7th Edition):
Adams, Laura. “Enzyme selection for optical mapping is hard.” 2015. Web. 21 Apr 2021.
Vancouver:
Adams L. Enzyme selection for optical mapping is hard. [Internet] [Masters thesis]. Colorado State University; 2015. [cited 2021 Apr 21].
Available from: http://hdl.handle.net/10217/167113.
Council of Science Editors:
Adams L. Enzyme selection for optical mapping is hard. [Masters Thesis]. Colorado State University; 2015. Available from: http://hdl.handle.net/10217/167113
Colorado State University
4.
Nowacki, Emily.
Meaning of work among cancer survivors: understanding critical paths to engagement.
Degree: MS(M.S.), Psychology, 2012, Colorado State University
URL: http://hdl.handle.net/10217/67894
► Though connections between meaningful work and employee engagement exist, almost no empirical research has examined this relationship. Both meaningful work and employee engagement have important…
(more)
▼ Though connections between meaningful work and employee engagement exist, almost no empirical research has examined this relationship. Both meaningful work and employee engagement have important implications for employees and their employing organizations, especially in the context of stressful events or circumstances. The present study adds to our knowledge as to how the two constructs might relate to each other, by examining a population that was hypothesized as facing great barriers to becoming engaged: cancer survivors. Data for this study were collected by conducting semi-structured in-person and phone interviews with 12 employed cancer survivors. Interviews were coded and analyzed using grounded theory techniques to determine how meaningful work relates to employee engagement in situations of duress. The results suggest that participants reframed or reappraised the meanings they found at work in several ways that implied engagement (e.g., motivation to continue working or return to work). Based on the results of this initial grounded theory study, propositions are made for future investigation.
Advisors/Committee Members: Byrne, Zinta (advisor), Howe, Adele (committee member), Vacha-Haase, Tammi (committee member).
Subjects/Keywords: cancer survivors; meaningful work; employee engagement
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APA (6th Edition):
Nowacki, E. (2012). Meaning of work among cancer survivors: understanding critical paths to engagement. (Masters Thesis). Colorado State University. Retrieved from http://hdl.handle.net/10217/67894
Chicago Manual of Style (16th Edition):
Nowacki, Emily. “Meaning of work among cancer survivors: understanding critical paths to engagement.” 2012. Masters Thesis, Colorado State University. Accessed April 21, 2021.
http://hdl.handle.net/10217/67894.
MLA Handbook (7th Edition):
Nowacki, Emily. “Meaning of work among cancer survivors: understanding critical paths to engagement.” 2012. Web. 21 Apr 2021.
Vancouver:
Nowacki E. Meaning of work among cancer survivors: understanding critical paths to engagement. [Internet] [Masters thesis]. Colorado State University; 2012. [cited 2021 Apr 21].
Available from: http://hdl.handle.net/10217/67894.
Council of Science Editors:
Nowacki E. Meaning of work among cancer survivors: understanding critical paths to engagement. [Masters Thesis]. Colorado State University; 2012. Available from: http://hdl.handle.net/10217/67894
Colorado State University
5.
O'Hara, Stephen.
Scalable learning of actions from unlabeled videos.
Degree: PhD, Computer Science, 2013, Colorado State University
URL: http://hdl.handle.net/10217/78864
► Emerging applications in human-computer interfaces, security, and robotics have a need for understanding human behavior from video data. Much of the research in the field…
(more)
▼ Emerging applications in human-computer interfaces, security, and robotics have a need for understanding human behavior from video data. Much of the research in the field of action recognition evaluates methods using relatively small data sets, under controlled conditions, and with a small set of allowable action labels. There are significant challenges in trying to adapt existing action recognition models to less structured and larger-scale data sets. Those challenges include: the recognition of a large vocabulary of actions, the scalability to learn from a large corpus of video data, the need for real-time recognition on streaming video, and the requirement to operate in settings with uncontrolled lighting, a variety of camera angles, dynamic backgrounds, and multiple actors. This thesis focuses on scalable methods for classifying and clustering actions with minimal human supervision. Unsupervised methods are emphasized in order to learn from a massive amount of unlabeled data, and for the potential to retrain models with minimal human intervention when adapting to new settings or applications. Because many applications of action recognition require real-time performance, and training data sets can be large, scalable methods for both learning and detection are beneficial. The specific contributions from this dissertation include a novel method for Approximate Nearest Neighbor (ANN) indexing of general metric spaces and the application of this structure to a manifold-based action representation. With this structure, nearest-neighbor action recognition is demonstrated to be comparable or superior to existing methods, while also being fast and scalable. Leveraging the same metric space indexing mechanism, a novel clustering method is introduced for discovering action exemplars in data.
Advisors/Committee Members: Draper, Bruce A. (advisor), Howe, Adele (committee member), Anderson, Charles (committee member), Peterson, Christopher (committee member).
Subjects/Keywords: action recognition; approximate nearest neighbor; Grassmann manifold; randomized forests; unsupervised learning; video analysis
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APA (6th Edition):
O'Hara, S. (2013). Scalable learning of actions from unlabeled videos. (Doctoral Dissertation). Colorado State University. Retrieved from http://hdl.handle.net/10217/78864
Chicago Manual of Style (16th Edition):
O'Hara, Stephen. “Scalable learning of actions from unlabeled videos.” 2013. Doctoral Dissertation, Colorado State University. Accessed April 21, 2021.
http://hdl.handle.net/10217/78864.
MLA Handbook (7th Edition):
O'Hara, Stephen. “Scalable learning of actions from unlabeled videos.” 2013. Web. 21 Apr 2021.
Vancouver:
O'Hara S. Scalable learning of actions from unlabeled videos. [Internet] [Doctoral dissertation]. Colorado State University; 2013. [cited 2021 Apr 21].
Available from: http://hdl.handle.net/10217/78864.
Council of Science Editors:
O'Hara S. Scalable learning of actions from unlabeled videos. [Doctoral Dissertation]. Colorado State University; 2013. Available from: http://hdl.handle.net/10217/78864
Colorado State University
6.
Hains, Doug.
Structure in combinatorial optimization and its effect on heuristic performance.
Degree: PhD, Computer Science, 2013, Colorado State University
URL: http://hdl.handle.net/10217/80944
► The goal in combinatorial optimization is to find a good solution among a finite set of solutions. In many combinatorial problems, the set of solutions…
(more)
▼ The goal in combinatorial optimization is to find a good solution among a finite set of solutions. In many combinatorial problems, the set of solutions scales at an exponential or greater rate with the instance size. The maximum boolean satisfiability (MAX-SAT) is one such problem that has many important theoretical and practical applications. Due to the exponential growth of the search space, sufficiently large instances of MAX-SAT are intractable for complete solvers. Incomplete solvers, such as stochastic local search (SLS) algorithms are necessary to find solutions in these cases. Many SLS algorithms for MAX-SAT have been developed on randomly generated benchmarks using a uniform distribution. As a result, SLS algorithms for MAX-SAT perform exceptionally well on uniform random instances. However, instances from real-world applications of MAX-SAT have a structure that is not captured in expectation by uniform random problems. The same SLS algorithms that perform well on uniform instances have a drastic drop in performance on structured instances. To better understand the performance drop on structured instances, we examine three characteristics commonly found in real-world applications of MAX-SAT: a power-law distribution of variables, clause lengths following a power-law distribution, and a community structure similar to that found in small-world models. We find that those instances with a community structure and clause lengths following a power-law distribution have a significantly more rugged search space and larger backbones than uniform random instances. These search space properties make it more difficult for SLS algorithms to find good solutions and in part explains the performance drop on industrial instances. In light of these findings, we examine two ways of improving the performance of SLS algorithms on industrial instances. First, we present a method of tractably computing the average evaluation of solutions in a subspace that we call a hyperplane. These averages can be used to estimate the correct setting of the backbone variables, with as high as 90% accuracy on industrial-like instances. By initializing SLS algorithms with these solutions, the search is able to find significantly better solutions than using standard initialization methods. Second, we re-examine the trade-offs between first and best improving search. We find that in many cases, the evaluation of solutions found by SLS algorithms using first improving search are no worse, and sometimes better, than those found by best improving. First improving search is significantly faster; using first improving search with AdaptG2WSAT, a
state-of-the-art SLS algorithm for MAX-SAT, gives us more than a 1,000x speedup on large industrial instances. Finally, we use our hyperplane averages to improve the performance of complete solvers of the satisfiability problem (SAT), the decision version of MAX-SAT. We use the averages to heuristically select a promising hyperplane and perform a reduction of the original problem based on the chosen hyperplane.…
Advisors/Committee Members: Whitley, Darrell (advisor), Howe, Adele (advisor), Bohm, Wim (committee member), Chong, Edwin (committee member).
Subjects/Keywords: combinatorial optimization; satisfiability; local search
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APA (6th Edition):
Hains, D. (2013). Structure in combinatorial optimization and its effect on heuristic performance. (Doctoral Dissertation). Colorado State University. Retrieved from http://hdl.handle.net/10217/80944
Chicago Manual of Style (16th Edition):
Hains, Doug. “Structure in combinatorial optimization and its effect on heuristic performance.” 2013. Doctoral Dissertation, Colorado State University. Accessed April 21, 2021.
http://hdl.handle.net/10217/80944.
MLA Handbook (7th Edition):
Hains, Doug. “Structure in combinatorial optimization and its effect on heuristic performance.” 2013. Web. 21 Apr 2021.
Vancouver:
Hains D. Structure in combinatorial optimization and its effect on heuristic performance. [Internet] [Doctoral dissertation]. Colorado State University; 2013. [cited 2021 Apr 21].
Available from: http://hdl.handle.net/10217/80944.
Council of Science Editors:
Hains D. Structure in combinatorial optimization and its effect on heuristic performance. [Doctoral Dissertation]. Colorado State University; 2013. Available from: http://hdl.handle.net/10217/80944
Colorado State University
7.
Hains, Douglas R.
Generalized partition crossover for the traveling salesman problem.
Degree: MS(M.S.), Computer Science, 2011, Colorado State University
URL: http://hdl.handle.net/10217/47314
► The Traveling Salesman Problem (TSP) is a well-studied combinatorial optimization problem with a wide spectrum of applications and theoretical value. We have designed a new…
(more)
▼ The Traveling Salesman Problem (TSP) is a well-studied combinatorial optimization problem with a wide spectrum of applications and theoretical value. We have designed a new recombination operator known as Generalized Partition Crossover (GPX) for the TSP. GPX is unique among other recombination operators for the TSP in that recombining two local optima produces new local optima with a high probability. Thus the operator can 'tunnel' between local optima without the need for intermediary solutions. The operator is respectful, meaning that any edges common between the two parent solutions are present in the offspring, and transmits alleles, meaning that offspring are comprised only of edges found in the parent solutions. We design a hybrid genetic algorithm, which uses local search in addition to recombination and selection, specifically for GPX. We show that this algorithm outperforms Chained Lin-Kernighan, a
state-of-the-art approximation algorithm for the TSP. We next analyze these algorithms to determine why the algorithms are not capable of consistently finding a globally optimal solution. Our results reveal a search space structure which we call 'funnels' because they are analogous to the funnels found in continuous optimization. Funnels are clusters of tours in the search space that are separated from one another by a non-trivial distance. We find that funnels can trap Chained Lin-Kernighan, preventing the search from finding an optimal solution. Our data indicate that, under certain conditions, GPX can tunnel between funnels, explaining the higher frequency of optimal solutions produced by our hybrid genetic algorithm using GPX.
Advisors/Committee Members: Whitley, L. Darrell (advisor), Howe, Adele E. (committee member), Mueller, Jennifer L. (committee member).
Subjects/Keywords: genetic algorithms; Traveling Salesman Problem; search space; local search
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APA (6th Edition):
Hains, D. R. (2011). Generalized partition crossover for the traveling salesman problem. (Masters Thesis). Colorado State University. Retrieved from http://hdl.handle.net/10217/47314
Chicago Manual of Style (16th Edition):
Hains, Douglas R. “Generalized partition crossover for the traveling salesman problem.” 2011. Masters Thesis, Colorado State University. Accessed April 21, 2021.
http://hdl.handle.net/10217/47314.
MLA Handbook (7th Edition):
Hains, Douglas R. “Generalized partition crossover for the traveling salesman problem.” 2011. Web. 21 Apr 2021.
Vancouver:
Hains DR. Generalized partition crossover for the traveling salesman problem. [Internet] [Masters thesis]. Colorado State University; 2011. [cited 2021 Apr 21].
Available from: http://hdl.handle.net/10217/47314.
Council of Science Editors:
Hains DR. Generalized partition crossover for the traveling salesman problem. [Masters Thesis]. Colorado State University; 2011. Available from: http://hdl.handle.net/10217/47314
Colorado State University
8.
Wigness, Maggie.
Evaluating cluster quality for visual data.
Degree: MS(M.S.), Computer Science, 2013, Colorado State University
URL: http://hdl.handle.net/10217/79204
► Digital video cameras have made it easy to collect large amounts of unlabeled data that can be used to learn to recognize objects and actions.…
(more)
▼ Digital video cameras have made it easy to collect large amounts of unlabeled data that can be used to learn to recognize objects and actions. Collecting ground-truth labels for this data, however, is a much more time consuming task that requires human intervention. One approach to train on this data, while keeping the human workload to a minimum, is to cluster the unlabeled samples, evaluate the quality of the clusters, and then ask a human annotator to label only the clusters believed to be dominated by a single object/action class. This thesis addresses the task of evaluating the quality of unlabeled image clusters. We compare four cluster quality measures (and a baseline method) using real-world and synthetic data sets. Three of these measures can be found in the existing data mining literature: Dunn Index, Davies-Bouldin Index and Silhouette Width. We introduce a novel cluster quality measure as the fourth measure, derived from recent advances in approximate nearest neighbor algorithms from the computer vision literature, called Proximity Forest Connectivity (PFC). Experiments on real-world data show that no cluster quality measure performs "best" on all data sets; however, our novel PFC measure is always competitive and results in more top performances than any of the other measures. Results from synthetic data experiments show that while the data mining measures are susceptible to over-clustering typically required of visual data, PFC is much more robust. Further synthetic data experiments modeling features of visual data show that Davies-Bouldin is most robust to large amounts of class-specific noise. However, Davies-Bouldin, Silhouette and PFC all perform well in the presence of data with small amounts of class-specific noise, whereas Dunn struggles to perform better than random.
Advisors/Committee Members: Draper, Bruce (advisor), Beveridge, Ross (committee member), Howe, Adele (committee member), Peterson, Chris (committee member).
Subjects/Keywords: cluster quality measures; image clustering; computer vision
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APA (6th Edition):
Wigness, M. (2013). Evaluating cluster quality for visual data. (Masters Thesis). Colorado State University. Retrieved from http://hdl.handle.net/10217/79204
Chicago Manual of Style (16th Edition):
Wigness, Maggie. “Evaluating cluster quality for visual data.” 2013. Masters Thesis, Colorado State University. Accessed April 21, 2021.
http://hdl.handle.net/10217/79204.
MLA Handbook (7th Edition):
Wigness, Maggie. “Evaluating cluster quality for visual data.” 2013. Web. 21 Apr 2021.
Vancouver:
Wigness M. Evaluating cluster quality for visual data. [Internet] [Masters thesis]. Colorado State University; 2013. [cited 2021 Apr 21].
Available from: http://hdl.handle.net/10217/79204.
Council of Science Editors:
Wigness M. Evaluating cluster quality for visual data. [Masters Thesis]. Colorado State University; 2013. Available from: http://hdl.handle.net/10217/79204
Colorado State University
9.
Chen, Wenxiang.
Step toward constant time local search for optimizing pseudo boolean functions, A.
Degree: MS(M.S.), Computer Science, 2013, Colorado State University
URL: http://hdl.handle.net/10217/81007
► Pseudo Boolean Functions (PBFs) are the objective functions for a wide class of hard optimization problems, such as MAX-SAT and MAX-CUT. Since these problems are…
(more)
▼ Pseudo Boolean Functions (PBFs) are the objective functions for a wide class of hard optimization problems, such as MAX-SAT and MAX-CUT. Since these problems are NP-Hard, researchers and practitioners rely on incomplete solvers, such as Stochastic Local Search (SLS), for large problems. Best-Improvement Local Search (BILS) is a common form of SLS, which always takes the move yielding the highest improvement in the objective function. Generally, the more runtime SLS is given, the better solution can be obtained. This thesis aims at algorithmically accelerating SLS for PBFs using Walsh Analysis. The contributions of this thesis are threefold. First, a general approach for executing an approximate best-improvement move in constant time on average using Walsh analysis, "Walsh-LS", is described. Conventional BILS typically requires examining all n neighbors to decide which move to take, given the number of variables is n. With Walsh analysis, however, we can determine which neighbors need to be checked. As long as the objective function is epistatically bounded by a constant k (k is the number of variables per subfunctions), the number of neighbors that need to be checked is constant regardless of problem size. An impressive speedup of runtime (up to 449 times) is observed in our empirical studies. Second, in the context of Walsh-LS, we empirically study two key components of SLS from the perspectives of both efficiency and effectiveness: 1) Local optimum escape method: hard random or soft restarts; 2) Local search strategy: first-improvement or best-improvement. Lastly, on average we can perform approximate BILS using the mean over a Hamming region of arbitrary radius as a surrogate objective function. Even though the number of points is exponential in the radius of the Hamming region, BILS using the mean value of points in the Hamming region as a surrogate objective function can still take each move in time independent of n on average. According to our empirical studies, using the average over a Hamming region as a surrogate objective function can yield superior performance results on neutral landscapes like NKq-landscapes.
Advisors/Committee Members: Whitley, L. Darrell (advisor), Howe, Adele E. (advisor), Cheney, Margaret (committee member).
Subjects/Keywords: complexity per move; stochastic local search; pseudo Boolean optimization; NK-landscapes
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APA ·
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APA (6th Edition):
Chen, W. (2013). Step toward constant time local search for optimizing pseudo boolean functions, A. (Masters Thesis). Colorado State University. Retrieved from http://hdl.handle.net/10217/81007
Chicago Manual of Style (16th Edition):
Chen, Wenxiang. “Step toward constant time local search for optimizing pseudo boolean functions, A.” 2013. Masters Thesis, Colorado State University. Accessed April 21, 2021.
http://hdl.handle.net/10217/81007.
MLA Handbook (7th Edition):
Chen, Wenxiang. “Step toward constant time local search for optimizing pseudo boolean functions, A.” 2013. Web. 21 Apr 2021.
Vancouver:
Chen W. Step toward constant time local search for optimizing pseudo boolean functions, A. [Internet] [Masters thesis]. Colorado State University; 2013. [cited 2021 Apr 21].
Available from: http://hdl.handle.net/10217/81007.
Council of Science Editors:
Chen W. Step toward constant time local search for optimizing pseudo boolean functions, A. [Masters Thesis]. Colorado State University; 2013. Available from: http://hdl.handle.net/10217/81007
Colorado State University
10.
Barber, Michael J.
Classification ensemble methods for mitigating concept drift within online data streams.
Degree: MS(M.S.), Computer Science, 2012, Colorado State University
URL: http://hdl.handle.net/10217/67994
► The task of instance classification within very large data streams is challenged by both the overwhelming amount of data, and a phenomenon known as concept…
(more)
▼ The task of instance classification within very large data streams is challenged by both the overwhelming amount of data, and a phenomenon known as concept drift. In this research we provide a comprehensive comparison of several
state of the art ensemble methods that purport to handle concept drift, and we propose two additional algorithms. Our two new methods, the AMPE and AMPE2 algorithms are then used to further our understanding of concept drift and the algorithmic factors that influence the performance of ensemble based concept drift algorithms.
Advisors/Committee Members: Howe, Adele E. (advisor), Anderson, Charles (committee member), Hoeting, Jennifer (committee member).
Subjects/Keywords: data mining; online analysis; machine learning; ensembles
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APA (6th Edition):
Barber, M. J. (2012). Classification ensemble methods for mitigating concept drift within online data streams. (Masters Thesis). Colorado State University. Retrieved from http://hdl.handle.net/10217/67994
Chicago Manual of Style (16th Edition):
Barber, Michael J. “Classification ensemble methods for mitigating concept drift within online data streams.” 2012. Masters Thesis, Colorado State University. Accessed April 21, 2021.
http://hdl.handle.net/10217/67994.
MLA Handbook (7th Edition):
Barber, Michael J. “Classification ensemble methods for mitigating concept drift within online data streams.” 2012. Web. 21 Apr 2021.
Vancouver:
Barber MJ. Classification ensemble methods for mitigating concept drift within online data streams. [Internet] [Masters thesis]. Colorado State University; 2012. [cited 2021 Apr 21].
Available from: http://hdl.handle.net/10217/67994.
Council of Science Editors:
Barber MJ. Classification ensemble methods for mitigating concept drift within online data streams. [Masters Thesis]. Colorado State University; 2012. Available from: http://hdl.handle.net/10217/67994
Colorado State University
11.
Sutton, Andrew M.
Analysis of combinatorial search spaces for a class of NP-hard problems, An.
Degree: PhD, Computer Science, 2011, Colorado State University
URL: http://hdl.handle.net/10217/50161
► Given a finite but very large set of states X and a real-valued objective function ƒ defined on X, combinatorial optimization refers to the problem…
(more)
▼ Given a finite but very large set of states X and a real-valued objective function ƒ defined on X, combinatorial optimization refers to the problem of finding elements of X that maximize (or minimize) ƒ. Many combinatorial search algorithms employ some perturbation operator to hill-climb in the search space. Such perturbative local search algorithms are
state of the art for many classes of NP-hard combinatorial optimization problems such as maximum k-satisfiability, scheduling, and problems of graph theory. In this thesis we analyze combinatorial search spaces by expanding the objective function into a (sparse) series of basis functions. While most analyses of the distribution of function values in the search space must rely on empirical sampling, the basis function expansion allows us to directly study the distribution of function values across regions of states for combinatorial problems without the need for sampling. We concentrate on objective functions that can be expressed as bounded pseudo-Boolean functions which are NP-hard to solve in general. We use the basis expansion to construct a polynomial-time algorithm for exactly computing constant-degree moments of the objective function ƒ over arbitrarily large regions of the search space. On functions with restricted codomains, these moments are related to the true distribution by a system of linear equations. Given low moments supplied by our algorithm, we construct bounds of the true distribution of ƒ over regions of the space using a linear programming approach. A straightforward relaxation allows us to efficiently approximate the distribution and hence quickly estimate the count of states in a given region that have certain values under the objective function. The analysis is also useful for characterizing properties of specific combinatorial problems. For instance, by connecting search space analysis to the theory of inapproximability, we prove that the bound specified by Grover's maximum principle for the Max-Ek-Lin-2 problem is sharp. Moreover, we use the framework to prove certain configurations are forbidden in regions of the Max-3-Sat search space, supplying the first theoretical confirmation of empirical results by others. Finally, we show that theoretical results can be used to drive the design of algorithms in a principled manner by using the search space analysis developed in this thesis in algorithmic applications. First, information obtained from our moment retrieving algorithm can be used to direct a hill-climbing search across plateaus in the Max-k-Sat search space. Second, the analysis can be used to control the mutation rate on a (1+1) evolutionary algorithm on bounded pseudo-Boolean functions so that the offspring of each search point is maximized in expectation. For these applications, knowledge of the search space structure supplied by the analysis translates to significant gains in the performance of search.
Advisors/Committee Members: Whitley, L. Darrell (advisor), Howe, Adele E. (advisor), Chong, Edwin K. P. (committee member), Bohm, A. P. Willem (committee member).
Subjects/Keywords: combinatorial optimization; combinatorial search; local search; pseudo-Boolean functions; search space analysis
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APA ·
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MLA ·
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APA (6th Edition):
Sutton, A. M. (2011). Analysis of combinatorial search spaces for a class of NP-hard problems, An. (Doctoral Dissertation). Colorado State University. Retrieved from http://hdl.handle.net/10217/50161
Chicago Manual of Style (16th Edition):
Sutton, Andrew M. “Analysis of combinatorial search spaces for a class of NP-hard problems, An.” 2011. Doctoral Dissertation, Colorado State University. Accessed April 21, 2021.
http://hdl.handle.net/10217/50161.
MLA Handbook (7th Edition):
Sutton, Andrew M. “Analysis of combinatorial search spaces for a class of NP-hard problems, An.” 2011. Web. 21 Apr 2021.
Vancouver:
Sutton AM. Analysis of combinatorial search spaces for a class of NP-hard problems, An. [Internet] [Doctoral dissertation]. Colorado State University; 2011. [cited 2021 Apr 21].
Available from: http://hdl.handle.net/10217/50161.
Council of Science Editors:
Sutton AM. Analysis of combinatorial search spaces for a class of NP-hard problems, An. [Doctoral Dissertation]. Colorado State University; 2011. Available from: http://hdl.handle.net/10217/50161
Colorado State University
12.
Teli, Mohammad Nayeem.
Face detection using correlation filters.
Degree: PhD, Computer Science, 2013, Colorado State University
URL: http://hdl.handle.net/10217/80983
► Cameras are ubiquitous and available all around us. As a result, images and videos are posted online in huge numbers. These images often need to…
(more)
▼ Cameras are ubiquitous and available all around us. As a result, images and videos are posted online in huge numbers. These images often need to be stored and analyzed. This requires the use of various computer vision applications that includes detection of human faces in these images and videos. The emphasis on face detection is evident from the applications found in everyday point and shoot cameras for a better focus, on social networking sites for tagging friends and family and for security situations which subsequently require face recognition or verification. This thesis focuses on detecting human faces in still images and video frames using correlation filters. These correlation filters are trained using a recent technique called Minimum Output Sum of Squared Error (MOSSE) developed by Bolme et al. Since correlation filters identify only a peak location, it only helps in localizing a single target point. In this thesis, I develop techniques to use this localization for detection of human faces of different scales and poses in uncontrolled background, location and lighting conditions. The goal of this research is to extend correlation filters for face detection and identify the scenarios where its potential is the most. The specific contributions of this work are the development of a novel face detector using correlation filters and the identification of the strengths and weaknesses of this approach. This approach is applied to an easy dataset and a hard dataset to emphasize the efficacy of correlations filters for face detection. This technique shows 95.6% accuracy in finding the exact location of the faces in images with controlled background and lighting. Although, the results on a hard dataset were not better than the OpenCV Viola and Jones face detector, it showed much better results, 81.5% detection rate compared to 69.43% detection rate by the Viola and Jones face detector, when tested on a customized dataset that was controlled for location change between training and test datasets. This result signifies the strength of a correlation based face detector in a specific scenario with uniform setting, such as a building entrance or an airport security gate.
Advisors/Committee Members: Beveridge, J. Ross (advisor), Draper, Bruce A. (committee member), Howe, Adele (committee member), Givens, Geof H. (committee member).
Subjects/Keywords: MOSSE; face detection; point and shoot; correlation filters; face
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APA (6th Edition):
Teli, M. N. (2013). Face detection using correlation filters. (Doctoral Dissertation). Colorado State University. Retrieved from http://hdl.handle.net/10217/80983
Chicago Manual of Style (16th Edition):
Teli, Mohammad Nayeem. “Face detection using correlation filters.” 2013. Doctoral Dissertation, Colorado State University. Accessed April 21, 2021.
http://hdl.handle.net/10217/80983.
MLA Handbook (7th Edition):
Teli, Mohammad Nayeem. “Face detection using correlation filters.” 2013. Web. 21 Apr 2021.
Vancouver:
Teli MN. Face detection using correlation filters. [Internet] [Doctoral dissertation]. Colorado State University; 2013. [cited 2021 Apr 21].
Available from: http://hdl.handle.net/10217/80983.
Council of Science Editors:
Teli MN. Face detection using correlation filters. [Doctoral Dissertation]. Colorado State University; 2013. Available from: http://hdl.handle.net/10217/80983
13.
Wigness, Maggie.
Hierarchical cluster guided labeling: efficient label collection for visual classification.
Degree: PhD, Computer Science, 2015, Colorado State University
URL: http://hdl.handle.net/10217/170415
► Visual classification is a core component in many visually intelligent systems. For example, recognition of objects and terrains provides perception during path planning and navigation…
(more)
▼ Visual classification is a core component in many visually intelligent systems. For example, recognition of objects and terrains provides perception during path planning and navigation tasks performed by autonomous agents. Supervised visual classifiers are typically trained with large sets of images to yield high classification performance. Although the collection of raw training data is easy, the required human effort to assign labels to this data is time consuming. This is particularly problematic in real-world applications with limited labeling time and resources. Techniques have emerged that are designed to help alleviate the labeling workload but suffer from several shortcomings. First, they do not generalize well to domains with limited a priori knowledge. Second, efficiency is achieved at the cost of collecting significant label noise which inhibits classifier learning or requires additional effort to remove. Finally, they introduce high latency between labeling queries, restricting real-world feasibility. This thesis addresses these shortcomings with unsupervised learning that exploits the hierarchical nature of feature patterns and semantic labels in visual data. Our hierarchical cluster guided labeling (HCGL) framework introduces a novel evaluation of hierarchical groupings to identify the most interesting changes in feature patterns. These changes help localize group selection in the hierarchy to discover and label a spectrum of visual semantics found in the data. We show that employing majority group-based labeling after selection allows HCGL to balance efficiency and label accuracy, yielding higher performing classifiers than other techniques with respect to labeling effort. Finally, we demonstrate the real-world feasibility of our labeling framework by quickly training high performing visual classifiers that aid in successful mobile robot path planning and navigation.
Advisors/Committee Members: Draper, Bruce (advisor), Beveridge, Ross (committee member), Howe, Adele (committee member), Peterson, Chris (committee member).
Subjects/Keywords: concept discovery; efficient label collection; hierarchical clustering; image classification
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Export
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APA (6th Edition):
Wigness, M. (2015). Hierarchical cluster guided labeling: efficient label collection for visual classification. (Doctoral Dissertation). Colorado State University. Retrieved from http://hdl.handle.net/10217/170415
Chicago Manual of Style (16th Edition):
Wigness, Maggie. “Hierarchical cluster guided labeling: efficient label collection for visual classification.” 2015. Doctoral Dissertation, Colorado State University. Accessed April 21, 2021.
http://hdl.handle.net/10217/170415.
MLA Handbook (7th Edition):
Wigness, Maggie. “Hierarchical cluster guided labeling: efficient label collection for visual classification.” 2015. Web. 21 Apr 2021.
Vancouver:
Wigness M. Hierarchical cluster guided labeling: efficient label collection for visual classification. [Internet] [Doctoral dissertation]. Colorado State University; 2015. [cited 2021 Apr 21].
Available from: http://hdl.handle.net/10217/170415.
Council of Science Editors:
Wigness M. Hierarchical cluster guided labeling: efficient label collection for visual classification. [Doctoral Dissertation]. Colorado State University; 2015. Available from: http://hdl.handle.net/10217/170415
Colorado State University
14.
Roberts, Mark.
Tale of 'T' metrics: choosing tradeoffs in multiobjective planning, A.
Degree: PhD, Computer Science, 2013, Colorado State University
URL: http://hdl.handle.net/10217/80970
Subjects/Keywords: artificial intelligence; classical planning
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APA (6th Edition):
Roberts, M. (2013). Tale of 'T' metrics: choosing tradeoffs in multiobjective planning, A. (Doctoral Dissertation). Colorado State University. Retrieved from http://hdl.handle.net/10217/80970
Chicago Manual of Style (16th Edition):
Roberts, Mark. “Tale of 'T' metrics: choosing tradeoffs in multiobjective planning, A.” 2013. Doctoral Dissertation, Colorado State University. Accessed April 21, 2021.
http://hdl.handle.net/10217/80970.
MLA Handbook (7th Edition):
Roberts, Mark. “Tale of 'T' metrics: choosing tradeoffs in multiobjective planning, A.” 2013. Web. 21 Apr 2021.
Vancouver:
Roberts M. Tale of 'T' metrics: choosing tradeoffs in multiobjective planning, A. [Internet] [Doctoral dissertation]. Colorado State University; 2013. [cited 2021 Apr 21].
Available from: http://hdl.handle.net/10217/80970.
Council of Science Editors:
Roberts M. Tale of 'T' metrics: choosing tradeoffs in multiobjective planning, A. [Doctoral Dissertation]. Colorado State University; 2013. Available from: http://hdl.handle.net/10217/80970
Colorado State University
15.
Sojda, Richard S.
Artificial intelligence based decision support for trumpeter swan management.
Degree: PhD, Forest Sciences, 2002, Colorado State University
URL: http://hdl.handle.net/10217/954
► The number of trumpeter swans (Cygnus buccinator) breeding in the Tri-State area where Montana, Idaho, and Wyoming come together has declined to just a few…
(more)
▼ The number of trumpeter swans (Cygnus buccinator) breeding in the Tri-
State area where Montana, Idaho, and Wyoming come together has declined to just a few hundred pairs. However, these birds are part of the Rocky Mountain Population which additionally has over 3,500 birds breeding in Alberta, British Columbia, Northwest Territories, and Yukon Territory. To a large degree, these birds seem to have abandoned traditional migratory pathways in the flyway. Waterfowl managers have been interested in decision support tools that would help them explore simulated management scenarios in their quest towards reaching population recovery and the reestablishment of traditional migratory pathways. I have developed a decision support system to assist biologists with such management, especially related to wetland ecology. Decision support systems use a combination of models, analytical techniques, and information retrieval to help develop and evaluate appropriate alternatives. Swan management is a domain that is ecologically complex, and this complexity is compounded by spatial and temporal issues. The Distributed Environment Centered Agent Framework (DECAF) was successful at integrating communications among agents, integrating ecological knowledge, and simulating swan distributions through implementation of a queuing system. The work I have conducted indicates a need for determining what other factors might allow a deeper understanding of the effects of management actions on the flyway distribution of waterfowl. Knowing those would allow the more refined development of algorithms for effective decision support systems via collaboration by intelligent agents. Additional, specific conclusions and ideas for future research related both to waterfowl ecology and to the use of multiagent systems have been triggered by the validation work.
Advisors/Committee Members: Dean, Denis J. (advisor), Fredrickson, Leigh H. (committee member), Howe, Adele E. (committee member), Loomis, John B. (committee member).
Subjects/Keywords: waterfowl ecology; Rocky Mountain trumpeter swans; ecological decision support systems; swan management; distributed environment centered agent framework; (DECAF); Trumpeter swan; Waterfowl management; Decision support systems; Cygnus buccinator
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APA (6th Edition):
Sojda, R. S. (2002). Artificial intelligence based decision support for trumpeter swan management. (Doctoral Dissertation). Colorado State University. Retrieved from http://hdl.handle.net/10217/954
Chicago Manual of Style (16th Edition):
Sojda, Richard S. “Artificial intelligence based decision support for trumpeter swan management.” 2002. Doctoral Dissertation, Colorado State University. Accessed April 21, 2021.
http://hdl.handle.net/10217/954.
MLA Handbook (7th Edition):
Sojda, Richard S. “Artificial intelligence based decision support for trumpeter swan management.” 2002. Web. 21 Apr 2021.
Vancouver:
Sojda RS. Artificial intelligence based decision support for trumpeter swan management. [Internet] [Doctoral dissertation]. Colorado State University; 2002. [cited 2021 Apr 21].
Available from: http://hdl.handle.net/10217/954.
Council of Science Editors:
Sojda RS. Artificial intelligence based decision support for trumpeter swan management. [Doctoral Dissertation]. Colorado State University; 2002. Available from: http://hdl.handle.net/10217/954
Colorado State University
16.
Watson, Jean-Paul.
Empirical modeling and analysis of local search algorithms for the job-shop scheduling problem.
Degree: PhD, Computer Science, 2003, Colorado State University
URL: http://hdl.handle.net/10217/26811
► Local search algorithms are among the most effective approaches for locating high-quality solutions to a wide range of combinatorial optimization problems. However, our theoretical understanding…
(more)
▼ Local search algorithms are among the most effective approaches for locating high-quality solutions to a wide range of combinatorial optimization problems. However, our theoretical understanding of these algorithms is very limited, leading to significant problems for both researchers and practitioners. Specifically, the lack of a theory of local search impedes the development of more effective algorithms, prevents practitioners from identifying the algorithm most appropriate for a given problem, and allows widespread conjecture and misinformation regarding the benefits and/or drawbacks of particular algorithms. This thesis represents a significant step toward a theory of local search. Using empirical methods, we develop theoretical models of the behavior of four well-known local search algorithms: a random walk, tabu search, iterated local search, and simulated annealing. The analysis proceeds in the context of the well-known job-shop scheduling problem, one of the most difficult NP-hard problems encountered in practice. The large volume of prior research on the job-shop scheduling problem provides a diverse range of available algorithms and problem instances, in addition to numerous empirical observations regarding local search algorithm behavior; the latter are used to validate our behavioral models. We show that all four local search algorithms can be modeled with high fidelity using straightforward variations of a generalized one-dimensional Markov chain. The states in these models represent sets of solutions a given fixed distance from the nearest optimal solution. The transition probabilities in all of the models are remarkably similar, in that search is consistently biased toward solutions that are roughly equidistant from the nearest optimal solution and solutions that are maximally distant from the nearest optimal solution. Surprisingly, the qualitative form of the transition probabilities is simply due to the structure of the representation used to encode solutions: the binary hypercube. The models account for between 96% and 99% of the variability in the cost required to locate both optimal and sub-optimal solutions to a wide range of problem instances, and provide explanations for numerous phenomena related to problem difficulty for local search in the job-shop scheduling problem. In the course of our analysis, we also disprove many conjectures regarding the behavior and benefits of particular algorithms. Our research indicates that despite their effectiveness, local search algorithms for the job-shop scheduling problem exhibit surprisingly simple run-time dynamics. Further, we observe minimal differences between the dynamical behavior of different algorithms. As expected given similar run-time dynamics, although contrary to numerous reports appearing in the literature, we also show that the performance of different algorithms is largely indistinguishable. Ultimately, our behavioral models serve to unify and provide explanations for a large body of observations regarding problem difficulty for local…
Advisors/Committee Members: Howe, Adele E. (advisor), Whitley, L. Darrell (advisor), Chong, Edwin Kah Pin (committee member), Bohm, Anton Willem (committee member).
Subjects/Keywords: random walk; tabu search; job-shop scheduling problem; empirical methods; local search algorithms; iterated local search; simulated annealing; Production scheduling – Computer simulation
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APA ·
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APA (6th Edition):
Watson, J. (2003). Empirical modeling and analysis of local search algorithms for the job-shop scheduling problem. (Doctoral Dissertation). Colorado State University. Retrieved from http://hdl.handle.net/10217/26811
Chicago Manual of Style (16th Edition):
Watson, Jean-Paul. “Empirical modeling and analysis of local search algorithms for the job-shop scheduling problem.” 2003. Doctoral Dissertation, Colorado State University. Accessed April 21, 2021.
http://hdl.handle.net/10217/26811.
MLA Handbook (7th Edition):
Watson, Jean-Paul. “Empirical modeling and analysis of local search algorithms for the job-shop scheduling problem.” 2003. Web. 21 Apr 2021.
Vancouver:
Watson J. Empirical modeling and analysis of local search algorithms for the job-shop scheduling problem. [Internet] [Doctoral dissertation]. Colorado State University; 2003. [cited 2021 Apr 21].
Available from: http://hdl.handle.net/10217/26811.
Council of Science Editors:
Watson J. Empirical modeling and analysis of local search algorithms for the job-shop scheduling problem. [Doctoral Dissertation]. Colorado State University; 2003. Available from: http://hdl.handle.net/10217/26811
Colorado State University
17.
Kretchmar, R. Matthew.
Synthesis of reinforcement learning and robust control theory, A.
Degree: PhD, Computer Science, 2000, Colorado State University
URL: http://hdl.handle.net/10217/26305
► The pursuit of control algorithms with improved performance drives the entire control research community as well as large parts of the mathematics, engineering, and artificial…
(more)
▼ The pursuit of control algorithms with improved performance drives the entire control research community as well as large parts of the mathematics, engineering, and artificial intelligence research communities. A fundamental limitation on achieving control performance is the conflicting requirement of maintaining system stability. In general, the more aggressive is the controller, the better the control performance but also the closer to system instability. Robust control is a collection of theories, techniques, the tools that form one of the leading edge approaches to control. Most controllers are designed not on the physical plant to be controlled, but on a mathematical model of the plant; hence, these controllers often do not perform well on the physical plant and are sometimes unstable. Robust control overcomes this problem by adding uncertainty to the mathematical model. The result is a more general, less aggressive controller which performs well on the both the model and the physical plant. However, the robust control method also sacrifices some control performance in order to achieve its guarantees of stability. Reinforcement learning based neural networks offer some distinct advantages for improving control performance. Their nonlinearity enables the neural network to implement a wider range of control functions, and their adaptability permits them to improve control performance via on-line, trial-and-error learning. However, neuro-control is typically plagued by a lack of stability guarantees. Even momentary instability cannot be tolerated in most physical plants, and thus, the threat of instability prohibits the application of neuro-control in many situations. In this dissertation, we develop a stable neuro-control scheme by synthesizing the two fields of reinforcement learning and robust control theory. We provide a learning system with many of the advantages of neuro-control. Using functional uncertainty to represent the nonlinear and time-varying components of the neuro networks, we apply the robust control techniques to guarantee the stability of our neuro-controller. Our scheme provides stable control not only for a specific fixed-weight, neural network, but also for a neuro-controller in which the weights are changing during learning. Furthermore, we apply our stable neuro-controller to several control tasks to demonstrate that the theoretical stability guarantee is readily applicable to real-life control situations. We also discuss several problems we encounter and identify potential avenues of future research.
Advisors/Committee Members: Anderson, Charles (advisor), Howe, Adele E. (committee member), Whitley, L. Darrell (committee member), Young, Peter M. (committee member), Hittle, Douglas C. (committee member).
Subjects/Keywords: robust control theory; neuro-control scheme; neuro-controller; neural networks; Reinforcement learning; Control theory
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APA ·
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MLA ·
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APA (6th Edition):
Kretchmar, R. M. (2000). Synthesis of reinforcement learning and robust control theory, A. (Doctoral Dissertation). Colorado State University. Retrieved from http://hdl.handle.net/10217/26305
Chicago Manual of Style (16th Edition):
Kretchmar, R Matthew. “Synthesis of reinforcement learning and robust control theory, A.” 2000. Doctoral Dissertation, Colorado State University. Accessed April 21, 2021.
http://hdl.handle.net/10217/26305.
MLA Handbook (7th Edition):
Kretchmar, R Matthew. “Synthesis of reinforcement learning and robust control theory, A.” 2000. Web. 21 Apr 2021.
Vancouver:
Kretchmar RM. Synthesis of reinforcement learning and robust control theory, A. [Internet] [Doctoral dissertation]. Colorado State University; 2000. [cited 2021 Apr 21].
Available from: http://hdl.handle.net/10217/26305.
Council of Science Editors:
Kretchmar RM. Synthesis of reinforcement learning and robust control theory, A. [Doctoral Dissertation]. Colorado State University; 2000. Available from: http://hdl.handle.net/10217/26305
. 
Open Access Theses and Dissertations
