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You searched for +publisher:"University of Florida" +contributor:("Gader, Paul D."). Showing records 1 – 30 of 42 total matches.

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University of Florida

1. Bodla, Navaneeth Kumar. Representing a Stack of Images as a Single Surface.

Degree: MS, Computer Engineering - Computer and Information Science and Engineering, 2014, University of Florida

 In this thesis we investigate a way of treating individual images and stack of images as a surface. Irrespective of what type of an image… (more)

Subjects/Keywords: Dimensionality reduction; Entropy; Geometric angles; Geometry; Histograms; Image rotation; Images; Pixels; Principal components analysis; Surface areas; area  – image  – surface  – vision  – visualization

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APA (6th Edition):

Bodla, N. K. (2014). Representing a Stack of Images as a Single Surface. (Masters Thesis). University of Florida. Retrieved from http://ufdc.ufl.edu/UFE0046844

Chicago Manual of Style (16th Edition):

Bodla, Navaneeth Kumar. “Representing a Stack of Images as a Single Surface.” 2014. Masters Thesis, University of Florida. Accessed October 22, 2019. http://ufdc.ufl.edu/UFE0046844.

MLA Handbook (7th Edition):

Bodla, Navaneeth Kumar. “Representing a Stack of Images as a Single Surface.” 2014. Web. 22 Oct 2019.

Vancouver:

Bodla NK. Representing a Stack of Images as a Single Surface. [Internet] [Masters thesis]. University of Florida; 2014. [cited 2019 Oct 22]. Available from: http://ufdc.ufl.edu/UFE0046844.

Council of Science Editors:

Bodla NK. Representing a Stack of Images as a Single Surface. [Masters Thesis]. University of Florida; 2014. Available from: http://ufdc.ufl.edu/UFE0046844


University of Florida

2. Chandran,Manu. Analysis of Bayesian Group-Lasso in Regression Models.

Degree: MS, Electrical and Computer Engineering, 2011, University of Florida

 The Group-Lasso estimator, used in regression analysis, does not calculate the variance estimates of regression coefficients. Such estimates are important, since they represent the confidence… (more)

Subjects/Keywords: Datasets; Error rates; Group size; Group structure; Linear regression; Machine learning; Mathematical variables; Parametric models; Regression analysis; Statistical discrepancies; bayesian  – feature  – group  – lasso  – selection

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APA (6th Edition):

Chandran,Manu. (2011). Analysis of Bayesian Group-Lasso in Regression Models. (Masters Thesis). University of Florida. Retrieved from http://ufdc.ufl.edu/UFE0043490

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Chicago Manual of Style (16th Edition):

Chandran,Manu. “Analysis of Bayesian Group-Lasso in Regression Models.” 2011. Masters Thesis, University of Florida. Accessed October 22, 2019. http://ufdc.ufl.edu/UFE0043490.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

MLA Handbook (7th Edition):

Chandran,Manu. “Analysis of Bayesian Group-Lasso in Regression Models.” 2011. Web. 22 Oct 2019.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Vancouver:

Chandran,Manu. Analysis of Bayesian Group-Lasso in Regression Models. [Internet] [Masters thesis]. University of Florida; 2011. [cited 2019 Oct 22]. Available from: http://ufdc.ufl.edu/UFE0043490.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Council of Science Editors:

Chandran,Manu. Analysis of Bayesian Group-Lasso in Regression Models. [Masters Thesis]. University of Florida; 2011. Available from: http://ufdc.ufl.edu/UFE0043490

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete


University of Florida

3. P George, Clint. A Realm-based Question Answering System using Probabilistic Modeling.

Degree: MS, Computer Engineering - Computer and Information Science and Engineering, 2010, University of Florida

 Conventional search engines traditionally perform key-word based searches of web documents. Since the wealth of information from the web is huge and often represented in… (more)

Subjects/Keywords: Cosine function; Domain ontologies; Engines; Model trains; Ontology; Regression analysis; Tires; Web pages; Whales; Wikipedia; lda, lerning, qa, ranking, topics

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APA (6th Edition):

P George, C. (2010). A Realm-based Question Answering System using Probabilistic Modeling. (Masters Thesis). University of Florida. Retrieved from http://ufdc.ufl.edu/UFE0042585

Chicago Manual of Style (16th Edition):

P George, Clint. “A Realm-based Question Answering System using Probabilistic Modeling.” 2010. Masters Thesis, University of Florida. Accessed October 22, 2019. http://ufdc.ufl.edu/UFE0042585.

MLA Handbook (7th Edition):

P George, Clint. “A Realm-based Question Answering System using Probabilistic Modeling.” 2010. Web. 22 Oct 2019.

Vancouver:

P George C. A Realm-based Question Answering System using Probabilistic Modeling. [Internet] [Masters thesis]. University of Florida; 2010. [cited 2019 Oct 22]. Available from: http://ufdc.ufl.edu/UFE0042585.

Council of Science Editors:

P George C. A Realm-based Question Answering System using Probabilistic Modeling. [Masters Thesis]. University of Florida; 2010. Available from: http://ufdc.ufl.edu/UFE0042585


University of Florida

4. Posadas, Brianna B. Development of Detection Algorithms for Apple Disease Using Hyperspectral Data.

Degree: MS, Agricultural and Biological Engineering, 2016, University of Florida

 Fuji apples are one of the top selling exports for South Korea bringing in over $233.4 million in 2013. However, during the last few decades,… (more)

Subjects/Keywords: Apples; Dimensionality reduction; Diseases; Fungicides; Orchards; Pixels; Principal components analysis; Reflectance; Spices; Wavelengths; apples  – classifier  – hyperspectral  – imagery  – korea  – reflectance

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APA (6th Edition):

Posadas, B. B. (2016). Development of Detection Algorithms for Apple Disease Using Hyperspectral Data. (Masters Thesis). University of Florida. Retrieved from http://ufdc.ufl.edu/UFE0049996

Chicago Manual of Style (16th Edition):

Posadas, Brianna B. “Development of Detection Algorithms for Apple Disease Using Hyperspectral Data.” 2016. Masters Thesis, University of Florida. Accessed October 22, 2019. http://ufdc.ufl.edu/UFE0049996.

MLA Handbook (7th Edition):

Posadas, Brianna B. “Development of Detection Algorithms for Apple Disease Using Hyperspectral Data.” 2016. Web. 22 Oct 2019.

Vancouver:

Posadas BB. Development of Detection Algorithms for Apple Disease Using Hyperspectral Data. [Internet] [Masters thesis]. University of Florida; 2016. [cited 2019 Oct 22]. Available from: http://ufdc.ufl.edu/UFE0049996.

Council of Science Editors:

Posadas BB. Development of Detection Algorithms for Apple Disease Using Hyperspectral Data. [Masters Thesis]. University of Florida; 2016. Available from: http://ufdc.ufl.edu/UFE0049996


University of Florida

5. BAGRECHA,PRIYANK D. Correntropy for Shape-Based Matching and Retrieval of Objects in Color Images.

Degree: MS, Electrical and Computer Engineering, 2011, University of Florida

 The majority of shape matching algorithms use either shape landmarks or features extracted from the shape boundary. Automatic extraction of these landmarks or features from… (more)

Subjects/Keywords: Crabs; Databases; Density estimation; Entropy; Geometric shapes; Image databases; Image retrieval; Information retrieval; Snakes; Surface contours; ACTIVECONTOUR  – CORRENTROPY  – IMAGERETRIEVAL  – NONPARAMETRIC  – SHAPEMATCHING

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APA (6th Edition):

D, B. (2011). Correntropy for Shape-Based Matching and Retrieval of Objects in Color Images. (Masters Thesis). University of Florida. Retrieved from http://ufdc.ufl.edu/UFE0042296

Chicago Manual of Style (16th Edition):

D, BAGRECHA,PRIYANK. “Correntropy for Shape-Based Matching and Retrieval of Objects in Color Images.” 2011. Masters Thesis, University of Florida. Accessed October 22, 2019. http://ufdc.ufl.edu/UFE0042296.

MLA Handbook (7th Edition):

D, BAGRECHA,PRIYANK. “Correntropy for Shape-Based Matching and Retrieval of Objects in Color Images.” 2011. Web. 22 Oct 2019.

Vancouver:

D B. Correntropy for Shape-Based Matching and Retrieval of Objects in Color Images. [Internet] [Masters thesis]. University of Florida; 2011. [cited 2019 Oct 22]. Available from: http://ufdc.ufl.edu/UFE0042296.

Council of Science Editors:

D B. Correntropy for Shape-Based Matching and Retrieval of Objects in Color Images. [Masters Thesis]. University of Florida; 2011. Available from: http://ufdc.ufl.edu/UFE0042296


University of Florida

6. Dranishnikov, Dmitri Alexander. Bayesian Hyperspectral Unmixing with Multivariate Beta Distributions.

Degree: PhD, Computer Engineering - Computer and Information Science and Engineering, 2014, University of Florida

 Many existing geometrical and statistical methods for endmember detection and spectral unmixing for Hyperspectral Image (HSI) data focus on the Linear Mixing Model (LMM). However,… (more)

Subjects/Keywords: Covariance; Datasets; Estimation methods; Markov chains; Modeling; Parametric models; Pixels; Proportions; Sample size; Statistical discrepancies; bayesian  – beta  – copula  – covariance  – hyperspectral  – mcmc  – multivariate  – unmixing

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APA (6th Edition):

Dranishnikov, D. A. (2014). Bayesian Hyperspectral Unmixing with Multivariate Beta Distributions. (Doctoral Dissertation). University of Florida. Retrieved from http://ufdc.ufl.edu/UFE0046690

Chicago Manual of Style (16th Edition):

Dranishnikov, Dmitri Alexander. “Bayesian Hyperspectral Unmixing with Multivariate Beta Distributions.” 2014. Doctoral Dissertation, University of Florida. Accessed October 22, 2019. http://ufdc.ufl.edu/UFE0046690.

MLA Handbook (7th Edition):

Dranishnikov, Dmitri Alexander. “Bayesian Hyperspectral Unmixing with Multivariate Beta Distributions.” 2014. Web. 22 Oct 2019.

Vancouver:

Dranishnikov DA. Bayesian Hyperspectral Unmixing with Multivariate Beta Distributions. [Internet] [Doctoral dissertation]. University of Florida; 2014. [cited 2019 Oct 22]. Available from: http://ufdc.ufl.edu/UFE0046690.

Council of Science Editors:

Dranishnikov DA. Bayesian Hyperspectral Unmixing with Multivariate Beta Distributions. [Doctoral Dissertation]. University of Florida; 2014. Available from: http://ufdc.ufl.edu/UFE0046690


University of Florida

7. Fisher, Nicholas. A Kernel Approach to Learning a Neuron Model from Spike Train Data.

Degree: PhD, Computer Engineering - Computer and Information Science and Engineering, 2010, University of Florida

 A spiking neuron is a principal component of the brain and the nervous system. Understanding the characteristics of a single neuron as well as the… (more)

Subjects/Keywords: Approximation; Cell membranes; Histograms; Hyperplanes; Ions; Learning; Membrane potential; Modeling; Neurons; Synapses; classification, data, kernel, model, neuron, spike, spiking, time, train

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APA (6th Edition):

Fisher, N. (2010). A Kernel Approach to Learning a Neuron Model from Spike Train Data. (Doctoral Dissertation). University of Florida. Retrieved from http://ufdc.ufl.edu/UFE0042012

Chicago Manual of Style (16th Edition):

Fisher, Nicholas. “A Kernel Approach to Learning a Neuron Model from Spike Train Data.” 2010. Doctoral Dissertation, University of Florida. Accessed October 22, 2019. http://ufdc.ufl.edu/UFE0042012.

MLA Handbook (7th Edition):

Fisher, Nicholas. “A Kernel Approach to Learning a Neuron Model from Spike Train Data.” 2010. Web. 22 Oct 2019.

Vancouver:

Fisher N. A Kernel Approach to Learning a Neuron Model from Spike Train Data. [Internet] [Doctoral dissertation]. University of Florida; 2010. [cited 2019 Oct 22]. Available from: http://ufdc.ufl.edu/UFE0042012.

Council of Science Editors:

Fisher N. A Kernel Approach to Learning a Neuron Model from Spike Train Data. [Doctoral Dissertation]. University of Florida; 2010. Available from: http://ufdc.ufl.edu/UFE0042012


University of Florida

8. Cobb, James T. Sonar Image Modeling for Texture Discrimination and Classification.

Degree: PhD, Electrical and Computer Engineering, 2011, University of Florida

 High-resolution synthetic aperture sonar (SAS) systems yield finely detailedimages of sea bed environments. SAS image texture models must be capable ofrepresenting a wide variety of… (more)

Subjects/Keywords: Imaging; Mathematical independent variables; Ocean floor; Parametric models; Pixels; Rock textures; Sand ripples; Sonar; Statistical models; Statistics; autocorrelation  – expectation-maximization  – k-distribution  – segmentation  – sonar

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APA (6th Edition):

Cobb, J. T. (2011). Sonar Image Modeling for Texture Discrimination and Classification. (Doctoral Dissertation). University of Florida. Retrieved from http://ufdc.ufl.edu/UFE0043611

Chicago Manual of Style (16th Edition):

Cobb, James T. “Sonar Image Modeling for Texture Discrimination and Classification.” 2011. Doctoral Dissertation, University of Florida. Accessed October 22, 2019. http://ufdc.ufl.edu/UFE0043611.

MLA Handbook (7th Edition):

Cobb, James T. “Sonar Image Modeling for Texture Discrimination and Classification.” 2011. Web. 22 Oct 2019.

Vancouver:

Cobb JT. Sonar Image Modeling for Texture Discrimination and Classification. [Internet] [Doctoral dissertation]. University of Florida; 2011. [cited 2019 Oct 22]. Available from: http://ufdc.ufl.edu/UFE0043611.

Council of Science Editors:

Cobb JT. Sonar Image Modeling for Texture Discrimination and Classification. [Doctoral Dissertation]. University of Florida; 2011. Available from: http://ufdc.ufl.edu/UFE0043611


University of Florida

9. Mcleod, Adam M. Learning Control Policies from Demonstration in Continuous Sensory and Action Space.

Degree: PhD, Electrical and Computer Engineering, 2015, University of Florida

 Learning to control a bipedal robot is a difficult control task. Learning to do so rapidly or automatically is even more difficult. This dissertation discusses… (more)

Subjects/Keywords: Datasets; Learning; Learning rate; Lifelong learning; Machine learning; Mathematical vectors; Robotics; Robots; Sine waves; Walking; learning  – machine

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APA (6th Edition):

Mcleod, A. M. (2015). Learning Control Policies from Demonstration in Continuous Sensory and Action Space. (Doctoral Dissertation). University of Florida. Retrieved from http://ufdc.ufl.edu/UFE0049418

Chicago Manual of Style (16th Edition):

Mcleod, Adam M. “Learning Control Policies from Demonstration in Continuous Sensory and Action Space.” 2015. Doctoral Dissertation, University of Florida. Accessed October 22, 2019. http://ufdc.ufl.edu/UFE0049418.

MLA Handbook (7th Edition):

Mcleod, Adam M. “Learning Control Policies from Demonstration in Continuous Sensory and Action Space.” 2015. Web. 22 Oct 2019.

Vancouver:

Mcleod AM. Learning Control Policies from Demonstration in Continuous Sensory and Action Space. [Internet] [Doctoral dissertation]. University of Florida; 2015. [cited 2019 Oct 22]. Available from: http://ufdc.ufl.edu/UFE0049418.

Council of Science Editors:

Mcleod AM. Learning Control Policies from Demonstration in Continuous Sensory and Action Space. [Doctoral Dissertation]. University of Florida; 2015. Available from: http://ufdc.ufl.edu/UFE0049418


University of Florida

10. Gurumoorthy,Karthik S. A Schrodinger Wave Mechanics Formalism for the Eikonal Problem and Its Associated Gradient Density Computation.

Degree: PhD, Computer Engineering - Computer and Information Science and Engineering, 2011, University of Florida

Many computational techniques based on classical mechanics exist but surprisingly there isn't a concomitant Advisors/Committee Members: Banerjee, Arunava (committee chair), Ho, Jeffrey (committee member), Vemuri, Baba C (committee member), Gader, Paul D (committee member), Shabanov, Sergei (committee member).

Subjects/Keywords: Approximation; Distance functions; Eikonal equation; Error rates; Fourier transformations; Mathematics; Quantum mechanics; Steepest descent method; Wave equations; Wave functions

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APA (6th Edition):

S, G. (2011). A Schrodinger Wave Mechanics Formalism for the Eikonal Problem and Its Associated Gradient Density Computation. (Doctoral Dissertation). University of Florida. Retrieved from http://ufdc.ufl.edu/UFE0043099

Chicago Manual of Style (16th Edition):

S, Gurumoorthy,Karthik. “A Schrodinger Wave Mechanics Formalism for the Eikonal Problem and Its Associated Gradient Density Computation.” 2011. Doctoral Dissertation, University of Florida. Accessed October 22, 2019. http://ufdc.ufl.edu/UFE0043099.

MLA Handbook (7th Edition):

S, Gurumoorthy,Karthik. “A Schrodinger Wave Mechanics Formalism for the Eikonal Problem and Its Associated Gradient Density Computation.” 2011. Web. 22 Oct 2019.

Vancouver:

S G. A Schrodinger Wave Mechanics Formalism for the Eikonal Problem and Its Associated Gradient Density Computation. [Internet] [Doctoral dissertation]. University of Florida; 2011. [cited 2019 Oct 22]. Available from: http://ufdc.ufl.edu/UFE0043099.

Council of Science Editors:

S G. A Schrodinger Wave Mechanics Formalism for the Eikonal Problem and Its Associated Gradient Density Computation. [Doctoral Dissertation]. University of Florida; 2011. Available from: http://ufdc.ufl.edu/UFE0043099


University of Florida

11. Horton, Joshua A. Normalized Maximum Likelihood on Variable-Length Sequence Datasets.

Degree: PhD, Computer Engineering - Computer and Information Science and Engineering, 2014, University of Florida

 This work examines the minimal description length criterion and the normalized maximum likelihood distribution and derives extensions for applications in which data sequences are of… (more)

Subjects/Keywords: Datasets; Language; Linguistics; Mathematical sequences; Modeling; Parametric models; Statistical models; Syllables; Topology; Words; hidden-markov-models  – machine-learning  – model-selection  – normalized-maximum-likelihood

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APA (6th Edition):

Horton, J. A. (2014). Normalized Maximum Likelihood on Variable-Length Sequence Datasets. (Doctoral Dissertation). University of Florida. Retrieved from http://ufdc.ufl.edu/UFE0047146

Chicago Manual of Style (16th Edition):

Horton, Joshua A. “Normalized Maximum Likelihood on Variable-Length Sequence Datasets.” 2014. Doctoral Dissertation, University of Florida. Accessed October 22, 2019. http://ufdc.ufl.edu/UFE0047146.

MLA Handbook (7th Edition):

Horton, Joshua A. “Normalized Maximum Likelihood on Variable-Length Sequence Datasets.” 2014. Web. 22 Oct 2019.

Vancouver:

Horton JA. Normalized Maximum Likelihood on Variable-Length Sequence Datasets. [Internet] [Doctoral dissertation]. University of Florida; 2014. [cited 2019 Oct 22]. Available from: http://ufdc.ufl.edu/UFE0047146.

Council of Science Editors:

Horton JA. Normalized Maximum Likelihood on Variable-Length Sequence Datasets. [Doctoral Dissertation]. University of Florida; 2014. Available from: http://ufdc.ufl.edu/UFE0047146


University of Florida

12. Mazhar, Raazia. Optimized Dictionary Design and Classification Using the Matching Pursuits Dissimilarity Measure.

Degree: PhD, Computer Engineering - Computer and Information Science and Engineering, 2009, University of Florida

 Discrimination-based classifiers differentiate between two classes by drawing a decision boundary between their data members in the feature domain. These classifiers are capable of correctly… (more)

Subjects/Keywords: Approximation; Datasets; Information classification; Land mines; Learning; Learning modalities; Outliers; Signal processing; Signals; Test data; agglomeration, camp, classification, clustering, competitive, detection, dictionary, dissimilarity, enhanced, fuzzy, ksvd, learning, machine, matching, measure, outlier, prototype, pursuits

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APA (6th Edition):

Mazhar, R. (2009). Optimized Dictionary Design and Classification Using the Matching Pursuits Dissimilarity Measure. (Doctoral Dissertation). University of Florida. Retrieved from http://ufdc.ufl.edu/UFE0024309

Chicago Manual of Style (16th Edition):

Mazhar, Raazia. “Optimized Dictionary Design and Classification Using the Matching Pursuits Dissimilarity Measure.” 2009. Doctoral Dissertation, University of Florida. Accessed October 22, 2019. http://ufdc.ufl.edu/UFE0024309.

MLA Handbook (7th Edition):

Mazhar, Raazia. “Optimized Dictionary Design and Classification Using the Matching Pursuits Dissimilarity Measure.” 2009. Web. 22 Oct 2019.

Vancouver:

Mazhar R. Optimized Dictionary Design and Classification Using the Matching Pursuits Dissimilarity Measure. [Internet] [Doctoral dissertation]. University of Florida; 2009. [cited 2019 Oct 22]. Available from: http://ufdc.ufl.edu/UFE0024309.

Council of Science Editors:

Mazhar R. Optimized Dictionary Design and Classification Using the Matching Pursuits Dissimilarity Measure. [Doctoral Dissertation]. University of Florida; 2009. Available from: http://ufdc.ufl.edu/UFE0024309


University of Florida

13. Glenn, Taylor C. Context-Dependent Detection in Hyperspectral Imagery.

Degree: PhD, Computer Engineering - Computer and Information Science and Engineering, 2013, University of Florida

 Significant context information often exists in hyperspectral images, but the existing known-signature target detection techniques do not explicitly account for this fact and do not… (more)

Subjects/Keywords: Algorithms; Change detection; Covariance; Datasets; False alarms; Matched filters; Pixels; Remote sensing; Signals; Statistics; bayesian  – context-dependent  – detection  – fuzzy  – hyperspectral

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APA (6th Edition):

Glenn, T. C. (2013). Context-Dependent Detection in Hyperspectral Imagery. (Doctoral Dissertation). University of Florida. Retrieved from http://ufdc.ufl.edu/UFE0046170

Chicago Manual of Style (16th Edition):

Glenn, Taylor C. “Context-Dependent Detection in Hyperspectral Imagery.” 2013. Doctoral Dissertation, University of Florida. Accessed October 22, 2019. http://ufdc.ufl.edu/UFE0046170.

MLA Handbook (7th Edition):

Glenn, Taylor C. “Context-Dependent Detection in Hyperspectral Imagery.” 2013. Web. 22 Oct 2019.

Vancouver:

Glenn TC. Context-Dependent Detection in Hyperspectral Imagery. [Internet] [Doctoral dissertation]. University of Florida; 2013. [cited 2019 Oct 22]. Available from: http://ufdc.ufl.edu/UFE0046170.

Council of Science Editors:

Glenn TC. Context-Dependent Detection in Hyperspectral Imagery. [Doctoral Dissertation]. University of Florida; 2013. Available from: http://ufdc.ufl.edu/UFE0046170


University of Florida

14. Close, Ryan Russell. Endmember and Proportion Estimation Using Physics-Based Macroscopic and Microscopic Mixture Models.

Degree: PhD, Computer Engineering - Computer and Information Science and Engineering, 2011, University of Florida

 Methods of incorporating macroscopic and microscopic mixture models into hyperspectral image (HSI) endmember and proportion estimation is presented and discussed. These methods utilize the linear… (more)

Subjects/Keywords: Albedo; Datasets; Estimate reliability; Estimation methods; Image processing; Modeling; Pixels; Proportions; Reflectance; Remote sensing; endmember  – hyperspectral  – macroscopic  – microscopic  – mixtures  – nonlinear  – physics-based  – proportion  – unmix

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APA (6th Edition):

Close, R. R. (2011). Endmember and Proportion Estimation Using Physics-Based Macroscopic and Microscopic Mixture Models. (Doctoral Dissertation). University of Florida. Retrieved from http://ufdc.ufl.edu/UFE0043582

Chicago Manual of Style (16th Edition):

Close, Ryan Russell. “Endmember and Proportion Estimation Using Physics-Based Macroscopic and Microscopic Mixture Models.” 2011. Doctoral Dissertation, University of Florida. Accessed October 22, 2019. http://ufdc.ufl.edu/UFE0043582.

MLA Handbook (7th Edition):

Close, Ryan Russell. “Endmember and Proportion Estimation Using Physics-Based Macroscopic and Microscopic Mixture Models.” 2011. Web. 22 Oct 2019.

Vancouver:

Close RR. Endmember and Proportion Estimation Using Physics-Based Macroscopic and Microscopic Mixture Models. [Internet] [Doctoral dissertation]. University of Florida; 2011. [cited 2019 Oct 22]. Available from: http://ufdc.ufl.edu/UFE0043582.

Council of Science Editors:

Close RR. Endmember and Proportion Estimation Using Physics-Based Macroscopic and Microscopic Mixture Models. [Doctoral Dissertation]. University of Florida; 2011. Available from: http://ufdc.ufl.edu/UFE0043582


University of Florida

15. Vanderkraats, Nathan. A Novel Probabilistic Lower Bound on Mutual Information Applied to a Category-Based Framework for Spike Train Discrimination.

Degree: PhD, Computer Engineering - Computer and Information Science and Engineering, 2009, University of Florida

 We propose a framework by which the information transmission capacity of feedforward networks of spiking neurons can be measured for any discrete set of stimulus… (more)

Subjects/Keywords: Auditory nerve; Distribution functions; Entropy; Harmonics; Mathematical vectors; Modeling; Neurons; Random variables; Signals; Statistics

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APA (6th Edition):

Vanderkraats, N. (2009). A Novel Probabilistic Lower Bound on Mutual Information Applied to a Category-Based Framework for Spike Train Discrimination. (Doctoral Dissertation). University of Florida. Retrieved from http://ufdc.ufl.edu/UFE0041213

Chicago Manual of Style (16th Edition):

Vanderkraats, Nathan. “A Novel Probabilistic Lower Bound on Mutual Information Applied to a Category-Based Framework for Spike Train Discrimination.” 2009. Doctoral Dissertation, University of Florida. Accessed October 22, 2019. http://ufdc.ufl.edu/UFE0041213.

MLA Handbook (7th Edition):

Vanderkraats, Nathan. “A Novel Probabilistic Lower Bound on Mutual Information Applied to a Category-Based Framework for Spike Train Discrimination.” 2009. Web. 22 Oct 2019.

Vancouver:

Vanderkraats N. A Novel Probabilistic Lower Bound on Mutual Information Applied to a Category-Based Framework for Spike Train Discrimination. [Internet] [Doctoral dissertation]. University of Florida; 2009. [cited 2019 Oct 22]. Available from: http://ufdc.ufl.edu/UFE0041213.

Council of Science Editors:

Vanderkraats N. A Novel Probabilistic Lower Bound on Mutual Information Applied to a Category-Based Framework for Spike Train Discrimination. [Doctoral Dissertation]. University of Florida; 2009. Available from: http://ufdc.ufl.edu/UFE0041213


University of Florida

16. Yang, Ce. Spectral Analysis and Multispectral/Hyperspectral Imaging to Detect Blueberry Fruit Maturity Stages for Early Blueberry Yield Estimation.

Degree: PhD, Agricultural and Biological Engineering, 2013, University of Florida

 Blueberry industry has been increasingly important to both Florida and United States economically since 1990’s (USDA, 2012). Thanks to the friendly climate, blueberry harvesting window… (more)

Subjects/Keywords: Blueberries; Fruits; Imaging; Maturity stage; Modeling; Pixels; Reflectance; Spectral bands; Spectroscopy; Wavelengths; blueberry  – hyperspectral  – imaging  – multispectral  – spectroscopy

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APA (6th Edition):

Yang, C. (2013). Spectral Analysis and Multispectral/Hyperspectral Imaging to Detect Blueberry Fruit Maturity Stages for Early Blueberry Yield Estimation. (Doctoral Dissertation). University of Florida. Retrieved from http://ufdc.ufl.edu/UFE0046320

Chicago Manual of Style (16th Edition):

Yang, Ce. “Spectral Analysis and Multispectral/Hyperspectral Imaging to Detect Blueberry Fruit Maturity Stages for Early Blueberry Yield Estimation.” 2013. Doctoral Dissertation, University of Florida. Accessed October 22, 2019. http://ufdc.ufl.edu/UFE0046320.

MLA Handbook (7th Edition):

Yang, Ce. “Spectral Analysis and Multispectral/Hyperspectral Imaging to Detect Blueberry Fruit Maturity Stages for Early Blueberry Yield Estimation.” 2013. Web. 22 Oct 2019.

Vancouver:

Yang C. Spectral Analysis and Multispectral/Hyperspectral Imaging to Detect Blueberry Fruit Maturity Stages for Early Blueberry Yield Estimation. [Internet] [Doctoral dissertation]. University of Florida; 2013. [cited 2019 Oct 22]. Available from: http://ufdc.ufl.edu/UFE0046320.

Council of Science Editors:

Yang C. Spectral Analysis and Multispectral/Hyperspectral Imaging to Detect Blueberry Fruit Maturity Stages for Early Blueberry Yield Estimation. [Doctoral Dissertation]. University of Florida; 2013. Available from: http://ufdc.ufl.edu/UFE0046320


University of Florida

17. Zhang, Xuping. Automatic Feature Learning and Parameter Estimation for Hidden Markov Models Using MCE and Gibbs Sampling.

Degree: PhD, Computer Engineering - Computer and Information Science and Engineering, 2009, University of Florida

 Hidden Markov models (HMMs) are useful tools for landmine detection using Ground Penetrating Radar (GPR), as well as many other applications. The performance of HMMs… (more)

Subjects/Keywords: Datasets; Feature extraction; Land mines; Learning; Learning modalities; Machine learning; Markov models; Neural networks; Parametric models; Principal components analysis; bayesian, classfication, convolution, feature, gaussian, gibbs, hmm, learning, mcmc, morphological, owa, sampling

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APA (6th Edition):

Zhang, X. (2009). Automatic Feature Learning and Parameter Estimation for Hidden Markov Models Using MCE and Gibbs Sampling. (Doctoral Dissertation). University of Florida. Retrieved from http://ufdc.ufl.edu/UFE0041157

Chicago Manual of Style (16th Edition):

Zhang, Xuping. “Automatic Feature Learning and Parameter Estimation for Hidden Markov Models Using MCE and Gibbs Sampling.” 2009. Doctoral Dissertation, University of Florida. Accessed October 22, 2019. http://ufdc.ufl.edu/UFE0041157.

MLA Handbook (7th Edition):

Zhang, Xuping. “Automatic Feature Learning and Parameter Estimation for Hidden Markov Models Using MCE and Gibbs Sampling.” 2009. Web. 22 Oct 2019.

Vancouver:

Zhang X. Automatic Feature Learning and Parameter Estimation for Hidden Markov Models Using MCE and Gibbs Sampling. [Internet] [Doctoral dissertation]. University of Florida; 2009. [cited 2019 Oct 22]. Available from: http://ufdc.ufl.edu/UFE0041157.

Council of Science Editors:

Zhang X. Automatic Feature Learning and Parameter Estimation for Hidden Markov Models Using MCE and Gibbs Sampling. [Doctoral Dissertation]. University of Florida; 2009. Available from: http://ufdc.ufl.edu/UFE0041157


University of Florida

18. Heo, Gyeongyong. Robust kernel methods in context-dependent fusion.

Degree: PhD, Computer Engineering - Computer and Information Science and Engineering, 2009, University of Florida

 Combining classifiers, a common way to improve the performance of classification, has gained popularity in recent years. By combining classifiers, one can take advantage of… (more)

Subjects/Keywords: Covariance; Cumulative distribution functions; Datasets; Eigenvectors; Error rates; Experimental results; Land mines; Mathematical vectors; Objective functions; Principal components analysis; clustering, context, fusion, fuzzy, kernel, landmine, pca, robustness, svm

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APA (6th Edition):

Heo, G. (2009). Robust kernel methods in context-dependent fusion. (Doctoral Dissertation). University of Florida. Retrieved from http://ufdc.ufl.edu/UFE0041144

Chicago Manual of Style (16th Edition):

Heo, Gyeongyong. “Robust kernel methods in context-dependent fusion.” 2009. Doctoral Dissertation, University of Florida. Accessed October 22, 2019. http://ufdc.ufl.edu/UFE0041144.

MLA Handbook (7th Edition):

Heo, Gyeongyong. “Robust kernel methods in context-dependent fusion.” 2009. Web. 22 Oct 2019.

Vancouver:

Heo G. Robust kernel methods in context-dependent fusion. [Internet] [Doctoral dissertation]. University of Florida; 2009. [cited 2019 Oct 22]. Available from: http://ufdc.ufl.edu/UFE0041144.

Council of Science Editors:

Heo G. Robust kernel methods in context-dependent fusion. [Doctoral Dissertation]. University of Florida; 2009. Available from: http://ufdc.ufl.edu/UFE0041144


University of Florida

19. Ramachandran, Ganesan. Fast Physics-Based Methods for Wideband Electromagnetic Induction Data Analysis.

Degree: PhD, Electrical and Computer Engineering, 2010, University of Florida

 Abstract of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of… (more)

Subjects/Keywords: Datasets; Dielectric materials; Error rates; Estimation methods; Geometric angles; Information search; Land mines; Modeling; Parametric models; Sensors; electromagnetic, landmine, sparse, wideband

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APA (6th Edition):

Ramachandran, G. (2010). Fast Physics-Based Methods for Wideband Electromagnetic Induction Data Analysis. (Doctoral Dissertation). University of Florida. Retrieved from http://ufdc.ufl.edu/UFE0041627

Chicago Manual of Style (16th Edition):

Ramachandran, Ganesan. “Fast Physics-Based Methods for Wideband Electromagnetic Induction Data Analysis.” 2010. Doctoral Dissertation, University of Florida. Accessed October 22, 2019. http://ufdc.ufl.edu/UFE0041627.

MLA Handbook (7th Edition):

Ramachandran, Ganesan. “Fast Physics-Based Methods for Wideband Electromagnetic Induction Data Analysis.” 2010. Web. 22 Oct 2019.

Vancouver:

Ramachandran G. Fast Physics-Based Methods for Wideband Electromagnetic Induction Data Analysis. [Internet] [Doctoral dissertation]. University of Florida; 2010. [cited 2019 Oct 22]. Available from: http://ufdc.ufl.edu/UFE0041627.

Council of Science Editors:

Ramachandran G. Fast Physics-Based Methods for Wideband Electromagnetic Induction Data Analysis. [Doctoral Dissertation]. University of Florida; 2010. Available from: http://ufdc.ufl.edu/UFE0041627


University of Florida

20. Deng, Qi. New Nonsmooth Convex Optimization Methods for Machine Learning.

Degree: PhD, Computer Engineering - Computer and Information Science and Engineering, 2015, University of Florida

 Machine learning studies how to build models and develop algorithms that can learn from the data. Central to the machine learning area is the design… (more)

Subjects/Keywords: Algorithms; Approximation; Averaging methods; Coordinate systems; Data smoothing; Datasets; Linear regression; Machine learning; Saddle points; Search engine optimization; learning  – machine  – optimization

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APA (6th Edition):

Deng, Q. (2015). New Nonsmooth Convex Optimization Methods for Machine Learning. (Doctoral Dissertation). University of Florida. Retrieved from http://ufdc.ufl.edu/UFE0049608

Chicago Manual of Style (16th Edition):

Deng, Qi. “New Nonsmooth Convex Optimization Methods for Machine Learning.” 2015. Doctoral Dissertation, University of Florida. Accessed October 22, 2019. http://ufdc.ufl.edu/UFE0049608.

MLA Handbook (7th Edition):

Deng, Qi. “New Nonsmooth Convex Optimization Methods for Machine Learning.” 2015. Web. 22 Oct 2019.

Vancouver:

Deng Q. New Nonsmooth Convex Optimization Methods for Machine Learning. [Internet] [Doctoral dissertation]. University of Florida; 2015. [cited 2019 Oct 22]. Available from: http://ufdc.ufl.edu/UFE0049608.

Council of Science Editors:

Deng Q. New Nonsmooth Convex Optimization Methods for Machine Learning. [Doctoral Dissertation]. University of Florida; 2015. Available from: http://ufdc.ufl.edu/UFE0049608


University of Florida

21. Bae, Jihye. Kernel Temporal Differences for Reinforcement Learning with Applications to Brain Machine Interfaces.

Degree: PhD, Electrical and Computer Engineering, 2013, University of Florida

 Reinforcement learning brain machine interfaces (RLBMI) have been shown to be a promising avenue for practical implementations of BMIs. In the RLBMI, a computer agent… (more)

Subjects/Keywords: Algorithms; Approximation; Decryption; Error rates; Experimentation; Learning; Learning modalities; Learning rate; Machine learning; Monkeys; learning

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APA (6th Edition):

Bae, J. (2013). Kernel Temporal Differences for Reinforcement Learning with Applications to Brain Machine Interfaces. (Doctoral Dissertation). University of Florida. Retrieved from http://ufdc.ufl.edu/UFE0045881

Chicago Manual of Style (16th Edition):

Bae, Jihye. “Kernel Temporal Differences for Reinforcement Learning with Applications to Brain Machine Interfaces.” 2013. Doctoral Dissertation, University of Florida. Accessed October 22, 2019. http://ufdc.ufl.edu/UFE0045881.

MLA Handbook (7th Edition):

Bae, Jihye. “Kernel Temporal Differences for Reinforcement Learning with Applications to Brain Machine Interfaces.” 2013. Web. 22 Oct 2019.

Vancouver:

Bae J. Kernel Temporal Differences for Reinforcement Learning with Applications to Brain Machine Interfaces. [Internet] [Doctoral dissertation]. University of Florida; 2013. [cited 2019 Oct 22]. Available from: http://ufdc.ufl.edu/UFE0045881.

Council of Science Editors:

Bae J. Kernel Temporal Differences for Reinforcement Learning with Applications to Brain Machine Interfaces. [Doctoral Dissertation]. University of Florida; 2013. Available from: http://ufdc.ufl.edu/UFE0045881


University of Florida

22. Yuksel,Seniha E. Context-Based Classification Via Data-Dependent Mixtures of Logistic and Hidden Markov Model Classifiers.

Degree: PhD, Computer Engineering - Computer and Information Science and Engineering, 2011, University of Florida

 This research addresses the problems encountered when designing classifiers for classes that contain multiple subclasses whose characteristics are dependent on the context. It is sometimes… (more)

Subjects/Keywords: Datasets; Information classification; Land mines; Learning; Log integral function; Machine learning; Markov models; Neural networks; Time series; Time series models; based  – classification  – context  – experts  – hidden  – hmm  – markov  – me  – mhmme  – mixture  – model  – variational  – vmec

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APA (6th Edition):

E, Y. (2011). Context-Based Classification Via Data-Dependent Mixtures of Logistic and Hidden Markov Model Classifiers. (Doctoral Dissertation). University of Florida. Retrieved from http://ufdc.ufl.edu/UFE0043347

Chicago Manual of Style (16th Edition):

E, Yuksel,Seniha. “Context-Based Classification Via Data-Dependent Mixtures of Logistic and Hidden Markov Model Classifiers.” 2011. Doctoral Dissertation, University of Florida. Accessed October 22, 2019. http://ufdc.ufl.edu/UFE0043347.

MLA Handbook (7th Edition):

E, Yuksel,Seniha. “Context-Based Classification Via Data-Dependent Mixtures of Logistic and Hidden Markov Model Classifiers.” 2011. Web. 22 Oct 2019.

Vancouver:

E Y. Context-Based Classification Via Data-Dependent Mixtures of Logistic and Hidden Markov Model Classifiers. [Internet] [Doctoral dissertation]. University of Florida; 2011. [cited 2019 Oct 22]. Available from: http://ufdc.ufl.edu/UFE0043347.

Council of Science Editors:

E Y. Context-Based Classification Via Data-Dependent Mixtures of Logistic and Hidden Markov Model Classifiers. [Doctoral Dissertation]. University of Florida; 2011. Available from: http://ufdc.ufl.edu/UFE0043347


University of Florida

23. Krekeler, Carolyn R. A Bayesian Based Graphical Model Framework for Estimation and Forecast of Stream Flow.

Degree: PhD, Electrical and Computer Engineering, 2012, University of Florida

 For watershed monitoring and management, it is important to be able to predict with measurable accuracy the flow rates of major streams as a function… (more)

Subjects/Keywords: Bayesian networks; Forecasting models; Forecasting techniques; Modeling; Probability forecasts; Rain; Spatial models; Statistical forecasts; Statistical models; Stream flow; bayesian  – graphical  – models  – network  – prediction

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APA (6th Edition):

Krekeler, C. R. (2012). A Bayesian Based Graphical Model Framework for Estimation and Forecast of Stream Flow. (Doctoral Dissertation). University of Florida. Retrieved from http://ufdc.ufl.edu/UFE0043967

Chicago Manual of Style (16th Edition):

Krekeler, Carolyn R. “A Bayesian Based Graphical Model Framework for Estimation and Forecast of Stream Flow.” 2012. Doctoral Dissertation, University of Florida. Accessed October 22, 2019. http://ufdc.ufl.edu/UFE0043967.

MLA Handbook (7th Edition):

Krekeler, Carolyn R. “A Bayesian Based Graphical Model Framework for Estimation and Forecast of Stream Flow.” 2012. Web. 22 Oct 2019.

Vancouver:

Krekeler CR. A Bayesian Based Graphical Model Framework for Estimation and Forecast of Stream Flow. [Internet] [Doctoral dissertation]. University of Florida; 2012. [cited 2019 Oct 22]. Available from: http://ufdc.ufl.edu/UFE0043967.

Council of Science Editors:

Krekeler CR. A Bayesian Based Graphical Model Framework for Estimation and Forecast of Stream Flow. [Doctoral Dissertation]. University of Florida; 2012. Available from: http://ufdc.ufl.edu/UFE0043967


University of Florida

24. Rummelt, Nicholas. Array Set Addressing Enabling Efficient Hexagonally Sampled Image Processing.

Degree: PhD, Computer Engineering - Computer and Information Science and Engineering, 2010, University of Florida

 It has long been known that there are numerous advantages to sampling images hexagonally rather than rectangularly. However, due to various shortcomings of the addressing… (more)

Subjects/Keywords: Butterflies; Cartesianism; Coordinate systems; Edge detection; Fast Fourier transformations; Fourier transformations; Image filters; Image processing; Pixels; Signals; addressing, grid, hexagonal, image, lattice, optimal, processing, sampling

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APA (6th Edition):

Rummelt, N. (2010). Array Set Addressing Enabling Efficient Hexagonally Sampled Image Processing. (Doctoral Dissertation). University of Florida. Retrieved from http://ufdc.ufl.edu/UFE0042319

Chicago Manual of Style (16th Edition):

Rummelt, Nicholas. “Array Set Addressing Enabling Efficient Hexagonally Sampled Image Processing.” 2010. Doctoral Dissertation, University of Florida. Accessed October 22, 2019. http://ufdc.ufl.edu/UFE0042319.

MLA Handbook (7th Edition):

Rummelt, Nicholas. “Array Set Addressing Enabling Efficient Hexagonally Sampled Image Processing.” 2010. Web. 22 Oct 2019.

Vancouver:

Rummelt N. Array Set Addressing Enabling Efficient Hexagonally Sampled Image Processing. [Internet] [Doctoral dissertation]. University of Florida; 2010. [cited 2019 Oct 22]. Available from: http://ufdc.ufl.edu/UFE0042319.

Council of Science Editors:

Rummelt N. Array Set Addressing Enabling Efficient Hexagonally Sampled Image Processing. [Doctoral Dissertation]. University of Florida; 2010. Available from: http://ufdc.ufl.edu/UFE0042319


University of Florida

25. Smock, Brandon. A Perception-Centric Framework for Digital Timbre Manipulation in Music Composition.

Degree: PhD, Computer Engineering - Computer and Information Science and Engineering, 2014, University of Florida

 In this work, a new framework is developed for the unrestricted manipulation of timbre in musical composition. Unlike other perceptual musical attributes such as pitch… (more)

Subjects/Keywords: Auditory perception; Harmonics; Loudness; Musical instruments; Musical perception; Musical timbre; Signals; Sound; Sound pitch; Timbre; composition  – instantaneous  – mds  – music  – perception  – space  – timbre

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APA (6th Edition):

Smock, B. (2014). A Perception-Centric Framework for Digital Timbre Manipulation in Music Composition. (Doctoral Dissertation). University of Florida. Retrieved from http://ufdc.ufl.edu/UFE0046695

Chicago Manual of Style (16th Edition):

Smock, Brandon. “A Perception-Centric Framework for Digital Timbre Manipulation in Music Composition.” 2014. Doctoral Dissertation, University of Florida. Accessed October 22, 2019. http://ufdc.ufl.edu/UFE0046695.

MLA Handbook (7th Edition):

Smock, Brandon. “A Perception-Centric Framework for Digital Timbre Manipulation in Music Composition.” 2014. Web. 22 Oct 2019.

Vancouver:

Smock B. A Perception-Centric Framework for Digital Timbre Manipulation in Music Composition. [Internet] [Doctoral dissertation]. University of Florida; 2014. [cited 2019 Oct 22]. Available from: http://ufdc.ufl.edu/UFE0046695.

Council of Science Editors:

Smock B. A Perception-Centric Framework for Digital Timbre Manipulation in Music Composition. [Doctoral Dissertation]. University of Florida; 2014. Available from: http://ufdc.ufl.edu/UFE0046695


University of Florida

26. Dhurandhar, Amit. Semi-Analytical Method for Analyzing Models and Model Selection Measures.

Degree: PhD, Computer Engineering - Computer and Information Science and Engineering, 2009, University of Florida

 Semi-Analytical Method for Analyzing Models and Model Selection Measures Considering the large amounts of data that is collected everyday in various domains such as health… (more)

Subjects/Keywords: Covariance; Datasets; Decision trees; Distance functions; Error rates; Machine learning; Polynomials; Random variables; Sample size; Statistical discrepancies

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APA (6th Edition):

Dhurandhar, A. (2009). Semi-Analytical Method for Analyzing Models and Model Selection Measures. (Doctoral Dissertation). University of Florida. Retrieved from http://ufdc.ufl.edu/UFE0024733

Chicago Manual of Style (16th Edition):

Dhurandhar, Amit. “Semi-Analytical Method for Analyzing Models and Model Selection Measures.” 2009. Doctoral Dissertation, University of Florida. Accessed October 22, 2019. http://ufdc.ufl.edu/UFE0024733.

MLA Handbook (7th Edition):

Dhurandhar, Amit. “Semi-Analytical Method for Analyzing Models and Model Selection Measures.” 2009. Web. 22 Oct 2019.

Vancouver:

Dhurandhar A. Semi-Analytical Method for Analyzing Models and Model Selection Measures. [Internet] [Doctoral dissertation]. University of Florida; 2009. [cited 2019 Oct 22]. Available from: http://ufdc.ufl.edu/UFE0024733.

Council of Science Editors:

Dhurandhar A. Semi-Analytical Method for Analyzing Models and Model Selection Measures. [Doctoral Dissertation]. University of Florida; 2009. Available from: http://ufdc.ufl.edu/UFE0024733


University of Florida

27. Vikas, Vishesh. Vestibular Dynamic Inclinometer and Measurement of Inclination Parameters.

Degree: PhD, Mechanical Engineering - Mechanical and Aerospace Engineering, 2011, University of Florida

 A human body displays a remarkable quality of maintaining both static and dynamic equilibrium for a rigid body in unstable equilibrium (modeled as an inverted… (more)

Subjects/Keywords: Acceleration; Accelerometers; Angular acceleration; Angular velocity; Coordinate systems; Gyroscopes; Microelectromechanical systems; Resultants; Sensors; Simulations; inclinometer  – robotics  – sensor  – vestibular

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APA (6th Edition):

Vikas, V. (2011). Vestibular Dynamic Inclinometer and Measurement of Inclination Parameters. (Doctoral Dissertation). University of Florida. Retrieved from http://ufdc.ufl.edu/UFE0043745

Chicago Manual of Style (16th Edition):

Vikas, Vishesh. “Vestibular Dynamic Inclinometer and Measurement of Inclination Parameters.” 2011. Doctoral Dissertation, University of Florida. Accessed October 22, 2019. http://ufdc.ufl.edu/UFE0043745.

MLA Handbook (7th Edition):

Vikas, Vishesh. “Vestibular Dynamic Inclinometer and Measurement of Inclination Parameters.” 2011. Web. 22 Oct 2019.

Vancouver:

Vikas V. Vestibular Dynamic Inclinometer and Measurement of Inclination Parameters. [Internet] [Doctoral dissertation]. University of Florida; 2011. [cited 2019 Oct 22]. Available from: http://ufdc.ufl.edu/UFE0043745.

Council of Science Editors:

Vikas V. Vestibular Dynamic Inclinometer and Measurement of Inclination Parameters. [Doctoral Dissertation]. University of Florida; 2011. Available from: http://ufdc.ufl.edu/UFE0043745


University of Florida

28. Maissen, James R. Extensions of Group Actions and the Hilbert-Smith Conjecture.

Degree: PhD, Mathematics, 2013, University of Florida

 The Hilbert-Smith Conjecture proposes that every effective compact group action on a compact manifold is a Lie group. The conjecture is the generalization of Hilbert’s… (more)

Subjects/Keywords: Compactification; Continuous functions; Distance functions; Homeomorphism; Mathematics; Quotients; Solenoids; Subrings; Topological theorems; Topology; action  – conjecture  – group  – hilbert  – smith  – topology

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APA (6th Edition):

Maissen, J. R. (2013). Extensions of Group Actions and the Hilbert-Smith Conjecture. (Doctoral Dissertation). University of Florida. Retrieved from http://ufdc.ufl.edu/UFE0045160

Chicago Manual of Style (16th Edition):

Maissen, James R. “Extensions of Group Actions and the Hilbert-Smith Conjecture.” 2013. Doctoral Dissertation, University of Florida. Accessed October 22, 2019. http://ufdc.ufl.edu/UFE0045160.

MLA Handbook (7th Edition):

Maissen, James R. “Extensions of Group Actions and the Hilbert-Smith Conjecture.” 2013. Web. 22 Oct 2019.

Vancouver:

Maissen JR. Extensions of Group Actions and the Hilbert-Smith Conjecture. [Internet] [Doctoral dissertation]. University of Florida; 2013. [cited 2019 Oct 22]. Available from: http://ufdc.ufl.edu/UFE0045160.

Council of Science Editors:

Maissen JR. Extensions of Group Actions and the Hilbert-Smith Conjecture. [Doctoral Dissertation]. University of Florida; 2013. Available from: http://ufdc.ufl.edu/UFE0045160


University of Florida

29. Nandan, Manu. Fast SVM Training Using Approximate Extreme Points.

Degree: PhD, Computer Engineering - Computer and Information Science and Engineering, 2013, University of Florida

 Support vectors machines (SVMs) are widely applied machine learning algorithms that have many desirable characteristics. However, with the ever increasing size of datasets, it has… (more)

Subjects/Keywords: Algorithms; Approximation; Datasets; Factorization; International conferences; Linear programming; Machine learning; Mathematical vectors; Matrices; Polynomials; approximate  – extreme  – kernels  – svm

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APA (6th Edition):

Nandan, M. (2013). Fast SVM Training Using Approximate Extreme Points. (Doctoral Dissertation). University of Florida. Retrieved from http://ufdc.ufl.edu/UFE0046147

Chicago Manual of Style (16th Edition):

Nandan, Manu. “Fast SVM Training Using Approximate Extreme Points.” 2013. Doctoral Dissertation, University of Florida. Accessed October 22, 2019. http://ufdc.ufl.edu/UFE0046147.

MLA Handbook (7th Edition):

Nandan, Manu. “Fast SVM Training Using Approximate Extreme Points.” 2013. Web. 22 Oct 2019.

Vancouver:

Nandan M. Fast SVM Training Using Approximate Extreme Points. [Internet] [Doctoral dissertation]. University of Florida; 2013. [cited 2019 Oct 22]. Available from: http://ufdc.ufl.edu/UFE0046147.

Council of Science Editors:

Nandan M. Fast SVM Training Using Approximate Extreme Points. [Doctoral Dissertation]. University of Florida; 2013. Available from: http://ufdc.ufl.edu/UFE0046147

30. Shuaibu, Mubarakat. Detection of Apple Marssonina Blotch Disease Using Hyperspectral Data.

Degree: MS, Agricultural and Biological Engineering, 2016, University of Florida

 Apple Marssonina blotch (AMB) is one of the most devastating apple diseases in the world, and it has caused huge economic losses to countries like… (more)

Subjects/Keywords: Apples; Datasets; Diseases; Imaging; Leaves; Pixels; Reflectance; Spectral bands; Spectral reflectance; Vegetation; apple  – blotch  – diagnosis  – hyperspectral  – marssonina  – reflectance  – spectroradiometer

University of Florida in Partial Fulfillment of the Requirements for the Degree of Master of… 

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APA (6th Edition):

Shuaibu, M. (2016). Detection of Apple Marssonina Blotch Disease Using Hyperspectral Data. (Masters Thesis). University of Florida. Retrieved from http://ufdc.ufl.edu/UFE0050055

Chicago Manual of Style (16th Edition):

Shuaibu, Mubarakat. “Detection of Apple Marssonina Blotch Disease Using Hyperspectral Data.” 2016. Masters Thesis, University of Florida. Accessed October 22, 2019. http://ufdc.ufl.edu/UFE0050055.

MLA Handbook (7th Edition):

Shuaibu, Mubarakat. “Detection of Apple Marssonina Blotch Disease Using Hyperspectral Data.” 2016. Web. 22 Oct 2019.

Vancouver:

Shuaibu M. Detection of Apple Marssonina Blotch Disease Using Hyperspectral Data. [Internet] [Masters thesis]. University of Florida; 2016. [cited 2019 Oct 22]. Available from: http://ufdc.ufl.edu/UFE0050055.

Council of Science Editors:

Shuaibu M. Detection of Apple Marssonina Blotch Disease Using Hyperspectral Data. [Masters Thesis]. University of Florida; 2016. Available from: http://ufdc.ufl.edu/UFE0050055

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