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Colorado State University
1.
Remington, Jason Michael.
Distributed systems in small scale research environments: Hadoop and the EM algorithm.
Degree: MS(M.S.), Computer Science, 2011, Colorado State University
URL: http://hdl.handle.net/10217/46744
► Distributed systems are widely used in large scale high performance computing environments, and often conjure visions of enormous data centers full of thousands of networked…
(more)
▼ Distributed systems are widely used in large scale high performance computing environments, and often conjure visions of enormous data centers full of thousands of networked machines working together. Smaller research environments may not have access to such a data center, and many jobs in these environments may still take weeks or longer to complete. Systems that work well on hundreds or thousands of machines on Terabyte and larger data sets may not scale down to small environments with a couple dozen machines and gigabyte data sets. This research determines the viability of one such system in a small research environment in order to determine what issues arise when scaling down to such a small environment. Specifically, we use Hadoop to implement the Expectation Maximization algorithm, which is iterative, stateful, inherently parallel, and computationally expensive. We find that the lack of support for modeling data dependencies between records results in large amounts of network traffic, and that the lack of support for iterative Map/Reduce magnifies the overhead on jobs which require multiple iterations. These results expose key issues which need to be addressed for the distributed system to perform well in a small research environment.
Advisors/Committee Members: Draper, Bruce A. (Bruce Austin), 1962- (advisor), Böhm, Wim (advisor), Burns, Patrick J. (committee member).
Subjects/Keywords: small cluster; distributed systems; EM; expectation maximization; Hadoop
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APA (6th Edition):
Remington, J. M. (2011). Distributed systems in small scale research environments: Hadoop and the EM algorithm. (Masters Thesis). Colorado State University. Retrieved from http://hdl.handle.net/10217/46744
Chicago Manual of Style (16th Edition):
Remington, Jason Michael. “Distributed systems in small scale research environments: Hadoop and the EM algorithm.” 2011. Masters Thesis, Colorado State University. Accessed February 27, 2021.
http://hdl.handle.net/10217/46744.
MLA Handbook (7th Edition):
Remington, Jason Michael. “Distributed systems in small scale research environments: Hadoop and the EM algorithm.” 2011. Web. 27 Feb 2021.
Vancouver:
Remington JM. Distributed systems in small scale research environments: Hadoop and the EM algorithm. [Internet] [Masters thesis]. Colorado State University; 2011. [cited 2021 Feb 27].
Available from: http://hdl.handle.net/10217/46744.
Council of Science Editors:
Remington JM. Distributed systems in small scale research environments: Hadoop and the EM algorithm. [Masters Thesis]. Colorado State University; 2011. Available from: http://hdl.handle.net/10217/46744

Colorado State University
2.
Bolme, David Scott.
Theory and applications of optimized correlation output filters.
Degree: PhD, Computer Science, 2011, Colorado State University
URL: http://hdl.handle.net/10217/47326
► Correlation filters are a standard way to solve many problems in signal processing, image processing, and computer vision. This research introduces two new filter training…
(more)
▼ Correlation filters are a standard way to solve many problems in signal processing, image processing, and computer vision. This research introduces two new filter training techniques, called Average of Synthetic Exact Filters (ASEF) and Minimum Output Sum of Squared Error (MOSSE), which have produced filters that perform well on many object detection problems. Typically, correlation filters are created by cropping templates out of training images; however, these templates fail to adequately discriminate between targets and background in difficult detection scenarios. More advanced methods such as Synthetic Discriminant Functions (SDF), Minimum Average Correlation Energy (MACE), Unconstrained Minimum Average Correlation Energy (UMACE), and Optimal Tradeoff Filters (OTF) improve performance by controlling the response of the correlation peak, but they only loosely control the effect of the filters on the rest of the image. This research introduces a new approach to correlation filter training, which considers the entire image to image mapping known as cross-correlation. ASEF and MOSSE find filters that optimally map the input training images to user specified outputs. The goal is to produce strong correlation peaks for targets while suppressing the responses to background. Results in eye localization, person detection, and visual tracking indicate that these new filters outperform other advanced correlation filter training methods and even produce better results than much more complicated non-filter algorithms.
Advisors/Committee Members: Beveridge, J. Ross, 1957- (advisor), Draper, Bruce A. (Bruce Austin), 1962- (committee member), Strout, Michelle Mills (committee member), Kirby, Michael J. (committee member).
Subjects/Keywords: computer vision; object detection; correlation filters
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APA (6th Edition):
Bolme, D. S. (2011). Theory and applications of optimized correlation output filters. (Doctoral Dissertation). Colorado State University. Retrieved from http://hdl.handle.net/10217/47326
Chicago Manual of Style (16th Edition):
Bolme, David Scott. “Theory and applications of optimized correlation output filters.” 2011. Doctoral Dissertation, Colorado State University. Accessed February 27, 2021.
http://hdl.handle.net/10217/47326.
MLA Handbook (7th Edition):
Bolme, David Scott. “Theory and applications of optimized correlation output filters.” 2011. Web. 27 Feb 2021.
Vancouver:
Bolme DS. Theory and applications of optimized correlation output filters. [Internet] [Doctoral dissertation]. Colorado State University; 2011. [cited 2021 Feb 27].
Available from: http://hdl.handle.net/10217/47326.
Council of Science Editors:
Bolme DS. Theory and applications of optimized correlation output filters. [Doctoral Dissertation]. Colorado State University; 2011. Available from: http://hdl.handle.net/10217/47326

Colorado State University
3.
Stevens, John.
Analysis of grating cell features for texture discrimination, An.
Degree: MS(M.S.), Computer Science, 2010, Colorado State University
URL: http://hdl.handle.net/10217/41470
► The design of artificial vision systems has been influenced by knowledge of the early stages of processing in the human vision system. The discovery of…
(more)
▼ The design of artificial vision systems has been influenced by knowledge of the early stages of processing in the human vision system. The discovery of directionally sensitive cells in the human visual cortex lead to the theory of edge detection in computer vision, and the discovery that simple cell receptive fields can be modeled as Gabor filters has led to the development and use of Gabor jets. In this thesis, we evaluate a low-level image feature inspired by "grating" cells found in the human visual cortex. These cells, and the features based on them, detect spatial gratings–repeated patterns of light and dark bars–in their receptive fields. We evaluate the utility of grating cell model features to distinguish different textures using Fisher’s linear discriminant. It will be shown that the grating cell features contain significantly more distinguishing information than another standard Gabor-filter-based image feature.
Advisors/Committee Members: Draper, Bruce A. (Bruce Austin), 1962- (advisor), Troup, Lucy (advisor), Beveridge, J. Ross, 1957- (committee member).
Subjects/Keywords: Image analysis; Imaging systems; Computer vision
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APA (6th Edition):
Stevens, J. (2010). Analysis of grating cell features for texture discrimination, An. (Masters Thesis). Colorado State University. Retrieved from http://hdl.handle.net/10217/41470
Chicago Manual of Style (16th Edition):
Stevens, John. “Analysis of grating cell features for texture discrimination, An.” 2010. Masters Thesis, Colorado State University. Accessed February 27, 2021.
http://hdl.handle.net/10217/41470.
MLA Handbook (7th Edition):
Stevens, John. “Analysis of grating cell features for texture discrimination, An.” 2010. Web. 27 Feb 2021.
Vancouver:
Stevens J. Analysis of grating cell features for texture discrimination, An. [Internet] [Masters thesis]. Colorado State University; 2010. [cited 2021 Feb 27].
Available from: http://hdl.handle.net/10217/41470.
Council of Science Editors:
Stevens J. Analysis of grating cell features for texture discrimination, An. [Masters Thesis]. Colorado State University; 2010. Available from: http://hdl.handle.net/10217/41470

Colorado State University
4.
Comer, Thomson H.
Automatic prediction of interest point stability.
Degree: MS(M.S.), Computer Science, 2009, Colorado State University
URL: http://hdl.handle.net/10217/41108
► Many computer vision applications depend on interest point detectors as a primary means of dimensionality reduction. While many experiments have been done measuring the repeatability…
(more)
▼ Many computer vision applications depend on interest point detectors as a primary means of dimensionality reduction. While many experiments have been done measuring the repeatability of selective attention algorithms [MTS+05, BL02, CJ02, MP07, SMBI98], we are not aware of any method for predicting the repeatability of an individual interest point at runtime. In this work, we attempt to predict the individual repeatability of a set of 106 interest points produced by Lowe's SIFT algorithm [Low03], Mikolajczyk's Harris-Affine [Mik02], and Mikolajczyk and Schmid's Hessian-Affine [MS04]. These algorithms were chosen because of their performance and popularity. 17 relevant attributes are recorded at each interest point, including eigenvalues of the second moment matrix, Hessian matrix, and Laplacian-of-Gaussian score. A generalized linear model is used to predict the repeatability of interest points from their attributes. The relationship between interest point attributes proves to be weak, however the repeatability of an individual interest point can to some extent be influenced by attributes. A 4% improvement ofmean interest point repeatability is acquired through two related methods: the addition of five new thresholding decisions and through selecting the N best interest points as predicted by a GLM of the logarithm of all 17 interest points. A similar GLM with a smaller set of author-selected attributes has comparable performance. This research finds that improving interest point repeatability remains a hard problem, with an improvement of over 4% unlikely using the current methods for interest point detection. The lack of clear relationships between interest point attributes and repeatability indicates that there is a hole in selective attention research that may be attributable to scale space implementation.
Advisors/Committee Members: Draper, Bruce A. (Bruce Austin), 1962- (advisor), Monnier, Patrick (committee member), Beveridge, Ross (committee member).
Subjects/Keywords: Computer vision; Human face recognition (Computer science); Optical pattern recognition
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APA ·
Chicago ·
MLA ·
Vancouver ·
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Export
to Zotero / EndNote / Reference
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APA (6th Edition):
Comer, T. H. (2009). Automatic prediction of interest point stability. (Masters Thesis). Colorado State University. Retrieved from http://hdl.handle.net/10217/41108
Chicago Manual of Style (16th Edition):
Comer, Thomson H. “Automatic prediction of interest point stability.” 2009. Masters Thesis, Colorado State University. Accessed February 27, 2021.
http://hdl.handle.net/10217/41108.
MLA Handbook (7th Edition):
Comer, Thomson H. “Automatic prediction of interest point stability.” 2009. Web. 27 Feb 2021.
Vancouver:
Comer TH. Automatic prediction of interest point stability. [Internet] [Masters thesis]. Colorado State University; 2009. [cited 2021 Feb 27].
Available from: http://hdl.handle.net/10217/41108.
Council of Science Editors:
Comer TH. Automatic prediction of interest point stability. [Masters Thesis]. Colorado State University; 2009. Available from: http://hdl.handle.net/10217/41108

Colorado State University
5.
Kazeka, Alexander.
Visual location awareness for mobile robots using feature-based vision.
Degree: MS(M.S.), Computer Science, 2010, Colorado State University
URL: http://hdl.handle.net/10217/38186
► This thesis presents an evaluation of feature-based visual recognition paradigm for the task of mobile robot localization. Although many works describe feature-based visual robot localization,…
(more)
▼ This thesis presents an evaluation of feature-based visual recognition paradigm for the task of mobile robot localization. Although many works describe feature-based visual robot localization, they often do so using complex methods for map-building and position estimation which obscure the underlying vision systems' performance. One of the main contributions of this work is the development of an evaluation algorithm employing simple models for location awareness with focus on evaluating the underlying vision system. While SeeAsYou is used as a prototypical vision system for evaluation, the algorithm is designed to allow it to be used with other feature-based vision systems as well. The main result is that feature-based recognition with SeeAsYou provides some information but is not strong enough to reliably achieve location awareness without the temporal context. Adding a simple temporal model, however, suggests a more reliable localization performance.
Advisors/Committee Members: Draper, Bruce A. (Bruce Austin), 1962- (advisor), Maciejewski, Anthony A. (committee member), Anderson, Charles W. (committee member).
Subjects/Keywords: Mobile robots; Robot vision; Computer vision
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APA ·
Chicago ·
MLA ·
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Export
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APA (6th Edition):
Kazeka, A. (2010). Visual location awareness for mobile robots using feature-based vision. (Masters Thesis). Colorado State University. Retrieved from http://hdl.handle.net/10217/38186
Chicago Manual of Style (16th Edition):
Kazeka, Alexander. “Visual location awareness for mobile robots using feature-based vision.” 2010. Masters Thesis, Colorado State University. Accessed February 27, 2021.
http://hdl.handle.net/10217/38186.
MLA Handbook (7th Edition):
Kazeka, Alexander. “Visual location awareness for mobile robots using feature-based vision.” 2010. Web. 27 Feb 2021.
Vancouver:
Kazeka A. Visual location awareness for mobile robots using feature-based vision. [Internet] [Masters thesis]. Colorado State University; 2010. [cited 2021 Feb 27].
Available from: http://hdl.handle.net/10217/38186.
Council of Science Editors:
Kazeka A. Visual location awareness for mobile robots using feature-based vision. [Masters Thesis]. Colorado State University; 2010. Available from: http://hdl.handle.net/10217/38186

Colorado State University
6.
Lui, Yui Man.
Geometric methods on special manifolds for visual recognition.
Degree: PhD, Computer Science, 2010, Colorado State University
URL: http://hdl.handle.net/10217/39042
► Many computer vision methods assume that the underlying geometry of images is Euclidean. This assumption is generally not valid. Therefore, this dissertation introduces new nonlinear…
(more)
▼ Many computer vision methods assume that the underlying geometry of images is Euclidean. This assumption is generally not valid. Therefore, this dissertation introduces new nonlinear geometric frameworks based upon special manifolds, namely Graβmann and Stiefel manifolds, for visual recognition. The motivation for this thesis is driven by the intrinsic geometry of visual data in which the visual data can be either a still image or video. Visual data are represented as points in appropriately chosen parameter spaces. The idiosyncratic aspects of the data in these spaces are then exploited for pattern classification. Three major research results are presented in this dissertation: face recognition for illumination spaces on Stiefel manifolds, face recognition on Graβmann registration manifolds, and action classification on product manifolds. Previous work has shown that illumination cones are idiosyncratic for face recognition in illumination spaces. However, it has not been addressed how a single image relates to an illumination cone. In this dissertation, a Bayesian model is employed to relight a single image to a set of illuminated variants. The subspace formed by these illuminated variants is characterized on a Stiefel manifold. A new distance measure called Canonical Stiefel Quotient (CSQ) is introduced. CSQ performs two projections on a tangent space of a Stiefel manifold and uses the quotient for classification. The proposed method demonstrates that illumination cones can be synthesized by relighting a single image to a set of images, and the synthesized illumination cones are discriminative for face recognition. Experiments on the CMU-PIE and YaleB data sets reveal that CSQ not only achieves high recognition accuracies for generic faces but also is robust to the choice of training sets. Subspaces can be realized as points on Graβmann manifolds. Motivated by image perturbation and the geometry of Graβmann manifolds, we present a method called Graβmann Registration Manifolds (GRM) for face recognition. First, a tangent space is formed by a set of affine perturbed images where the tangent space admits a vector space structure. Second, the tangent spaces are embedded on a Graβmann manifold and chordal distance is used to compare subspaces. Experiments on the FERET database suggest that the proposed method yields excellent results using both holistic and local features. Specifically, on the FERET Dup2 data set, which is generally considered the most difficult data set on FERET, the proposed method achieves the highest rank one identification rate among all non-trained methods currently in the literature. Human actions compose a series of movements and can be described by a sequence of video frames. Since videos are multidimensional data, data tensors are the natural choice for data representation. In this dissertation, a data tensor is expressed as a point on a product manifold and classification is performed on this product space. First, we factorize a data tensor using a modified High Order Singular Value…
Advisors/Committee Members: Beveridge, J. Ross (advisor), Kirby, Michael, 1961- (committee member), Draper, Bruce A. (Bruce Austin), 1962- (committee member), Whitley, L. Darrell (committee member).
Subjects/Keywords: action classification; visual recognition; special manifolds; geometric methods; face recognition; Human face recognition (Computer science); Grassmann manifolds; Stiefel manifolds
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❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Lui, Y. M. (2010). Geometric methods on special manifolds for visual recognition. (Doctoral Dissertation). Colorado State University. Retrieved from http://hdl.handle.net/10217/39042
Chicago Manual of Style (16th Edition):
Lui, Yui Man. “Geometric methods on special manifolds for visual recognition.” 2010. Doctoral Dissertation, Colorado State University. Accessed February 27, 2021.
http://hdl.handle.net/10217/39042.
MLA Handbook (7th Edition):
Lui, Yui Man. “Geometric methods on special manifolds for visual recognition.” 2010. Web. 27 Feb 2021.
Vancouver:
Lui YM. Geometric methods on special manifolds for visual recognition. [Internet] [Doctoral dissertation]. Colorado State University; 2010. [cited 2021 Feb 27].
Available from: http://hdl.handle.net/10217/39042.
Council of Science Editors:
Lui YM. Geometric methods on special manifolds for visual recognition. [Doctoral Dissertation]. Colorado State University; 2010. Available from: http://hdl.handle.net/10217/39042

Colorado State University
7.
Crawford-Hines, Stewart.
Machine learned boundary definitions for an expert's tracing assistant in image processing.
Degree: PhD, Computer Science, 2003, Colorado State University
URL: http://hdl.handle.net/10217/28552
► Most image processing work addressing boundary definition tasks embeds the assumption that an edge in an image corresponds to the boundary of interest in the…
(more)
▼ Most image processing work addressing boundary definition tasks embeds the assumption that an edge in an image corresponds to the boundary of interest in the world. In straightforward imagery this is true, however it is not always the case. There are images in which edges are indistinct or obscure, and these images can only be segmented by a human expert. The work in this dissertation addresses the range of imagery between the two extremes of those straightforward images and those requiring human guidance to appropriately segment. By freeing systems of a priori edge definitions and building in a mechanism to learn the boundary definitions needed, systems can do better and be more broadly applicable. This dissertation presents the construction of such a boundary-learning system and demonstrates the validity of this premise on real data. A framework was created for the task in which expert-provided boundary exemplars are used to create training data, which in turn are used by a neural network to learn the task and replicate the expert's boundary tracing behavior. This is the framework for the Expert's Tracing Assistant (ETA) system. For a representative set of nine structures in the Visible Human imagery, ETA was compared and contrasted to two
state-of-the-art, user guided methods – Intelligent Scissors (IS) and Active Contour Models (ACM). Each method was used to define a boundary, and the distances between these boundaries and an expert's ground truth were compared. Across independent trials, there will be a natural variation in an expert's boundary tracing, and this degree of variation served as a benchmark against which these three methods were compared. For simple structural boundaries, all the methods were equivalent. However, in more difficult cases, ETA was shown to significantly better replicate the expert's boundary than either IS or ACM. In these cases, where the expert's judgement was most called into play to bound the structure, ACM and IS could not adapt to the boundary character used by the expert while ETA could.
Advisors/Committee Members: Anderson, Charles W. (advisor), Draper, Bruce A. (Bruce Austin), 1962- (committee member), Beveridge, J. Ross (committee member), Alciatore, David G. (committee member).
Subjects/Keywords: visible human imagery; boundary definitions; expert's tracing assistant; ETA; intelligent scissors; IS; active contour models; ACM; boundary-learning system; Image processing; Pattern recognition systems
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❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Crawford-Hines, S. (2003). Machine learned boundary definitions for an expert's tracing assistant in image processing. (Doctoral Dissertation). Colorado State University. Retrieved from http://hdl.handle.net/10217/28552
Chicago Manual of Style (16th Edition):
Crawford-Hines, Stewart. “Machine learned boundary definitions for an expert's tracing assistant in image processing.” 2003. Doctoral Dissertation, Colorado State University. Accessed February 27, 2021.
http://hdl.handle.net/10217/28552.
MLA Handbook (7th Edition):
Crawford-Hines, Stewart. “Machine learned boundary definitions for an expert's tracing assistant in image processing.” 2003. Web. 27 Feb 2021.
Vancouver:
Crawford-Hines S. Machine learned boundary definitions for an expert's tracing assistant in image processing. [Internet] [Doctoral dissertation]. Colorado State University; 2003. [cited 2021 Feb 27].
Available from: http://hdl.handle.net/10217/28552.
Council of Science Editors:
Crawford-Hines S. Machine learned boundary definitions for an expert's tracing assistant in image processing. [Doctoral Dissertation]. Colorado State University; 2003. Available from: http://hdl.handle.net/10217/28552

Colorado State University
8.
Bush, Keith A.
Echo state model of non-Markovian reinforcement learning, An.
Degree: PhD, Computer Science, 2008, Colorado State University
URL: http://hdl.handle.net/10217/28682
► There exists a growing need for intelligent, autonomous control strategies that operate in real-world domains. Theoretically the state-action space must exhibit the Markov property in…
(more)
▼ There exists a growing need for intelligent, autonomous control strategies that operate in real-world domains. Theoretically the
state-action space must exhibit the Markov property in order for reinforcement learning to be applicable. Empirical evidence, however, suggests that reinforcement learning also applies to domains where the
state-action space is approximately Markovian, a requirement for the overwhelming majority of real-world domains. These domains, termed non-Markovian reinforcement learning domains, raise a unique set of practical challenges. The reconstruction dimension required to approximate a Markovian
state-space is unknown a priori and can potentially be large. Further, spatial complexity of local function approximation of the reinforcement learning domain grows exponentially with the reconstruction dimension. Parameterized dynamic systems alleviate both embedding length and
state-space dimensionality concerns by reconstructing an approximate Markovian
state-space via a compact, recurrent representation. Yet this representation extracts a cost; modeling reinforcement learning domains via adaptive, parameterized dynamic systems is characterized by instability, slow-convergence, and high computational or spatial training complexity. The objectives of this research are to demonstrate a stable, convergent, accurate, and scalable model of non-Markovian reinforcement learning domains. These objectives are fulfilled via fixed point analysis of the dynamics underlying the reinforcement learning domain and the Echo
State Network, a class of parameterized dynamic system. Understanding models of non-Markovian reinforcement learning domains requires understanding the interactions between learning domains and their models. Fixed point analysis of the Mountain Car Problem reinforcement learning domain, for both local and nonlocal function approximations, suggests a close relationship between the locality of the approximation and the number and severity of bifurcations of the fixed point structure. This research suggests the likely cause of this relationship: reinforcement learning domains exist within a dynamic feature space in which trajectories are analogous to states. The fixed point structure maps dynamic space onto
state-space. This explanation suggests two testable hypotheses. Reinforcement learning is sensitive to
state-space locality because states cluster as trajectories in time rather than space. Second, models using trajectory-based features should exhibit good modeling performance and few changes in fixed point structure. Analysis of performance of lookup table, feedforward neural network, and Echo
State Network (ESN) on the Mountain Car Problem reinforcement learning domain confirm these hypotheses. The ESN is a large, sparse, randomly-generated, unadapted recurrent neural network, which adapts a linear projection of the target domain onto the hidden layer. ESN modeling results on reinforcement learning domains show it achieves performance comparable to lookup table and neural network architectures…
Advisors/Committee Members: Anderson, Charles W. (advisor), Draper, Bruce A. (Bruce Austin), 1962- (committee member), Kirby, Michael, 1961- (committee member), Young, Peter M. (committee member).
Subjects/Keywords: reinforcement learning (machine learning); mountain car problem; reinforcement learning; Markovian; echo state network; ESN; fixed point analysis; Hybrid systems
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❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Bush, K. A. (2008). Echo state model of non-Markovian reinforcement learning, An. (Doctoral Dissertation). Colorado State University. Retrieved from http://hdl.handle.net/10217/28682
Chicago Manual of Style (16th Edition):
Bush, Keith A. “Echo state model of non-Markovian reinforcement learning, An.” 2008. Doctoral Dissertation, Colorado State University. Accessed February 27, 2021.
http://hdl.handle.net/10217/28682.
MLA Handbook (7th Edition):
Bush, Keith A. “Echo state model of non-Markovian reinforcement learning, An.” 2008. Web. 27 Feb 2021.
Vancouver:
Bush KA. Echo state model of non-Markovian reinforcement learning, An. [Internet] [Doctoral dissertation]. Colorado State University; 2008. [cited 2021 Feb 27].
Available from: http://hdl.handle.net/10217/28682.
Council of Science Editors:
Bush KA. Echo state model of non-Markovian reinforcement learning, An. [Doctoral Dissertation]. Colorado State University; 2008. Available from: http://hdl.handle.net/10217/28682
.