Browsing Theses and Dissertations - Department of Information Systems, Statistics & Management Science by Author "Gray, J. Brian"

Browsing Theses and Dissertations - Department of Information Systems, Statistics & Management Science by Author "Gray, J. Brian"

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  • Wang, Ketong (University of Alabama Libraries, 2017)
    Predictive models and clustering algorithms are two of the most important statistical methodologies in solving quantitative problems. This dissertation document aims at proposing several innovative prediction and clustering ...
  • Boone, Jeffrey Michael (University of Alabama Libraries, 2010)
    Autocorrelated data are common in today's process control applications. Many of these applications involve two or more related variables so that multivariate statistical process control (SPC) methods should be used in ...
  • Oh, Dong-Yop (University of Alabama Libraries, 2012)
    Many simple and complex methods have been developed to solve the classification problem. Boosting is one of the best known techniques for improving the prediction accuracy of classification methods, but boosting is sometimes ...
  • Sasamoto, Mark Makoto (University of Alabama Libraries, 2010)
    Tree structured modeling is a data mining technique used to recursively partition a data set into relatively homogeneous subgroups in order to make more accurate predictions on future observations. One of the earliest ...
  • Choi, Hwanseok (University of Alabama Libraries, 2010)
    Clustering multivariate time series data has been a challenging task for researchers since data has multiple dimensions to consider such as auto-correlations and cross-correlations whereas multivariate time series data has ...
  • Ishfaq, Rafay (University of Alabama Libraries, 2010)
    This research is motivated by the extraordinary increase in the use of intermodal shipments in recent years for both domestic and global movement of freight. Three mathematical models, which explore the dynamics of intermodal ...
  • Michaelson, Gregory Vincent (University of Alabama Libraries, 2010)
    Determining the structure of large and complex networks is a problem that has stirred great interest in many fields including mathematics, computer science, sociology, biomedical research, and epidemiology. Despite this ...
  • Xu, Jie (University of Alabama Libraries, 2013)
    Ensemble models, such as bagging (Breiman, 1996), random forests (Breiman, 2001a), and boosting (Freund and Schapire, 1997), have better predictive accuracy than single classifiers. These ensembles typically consist of ...
  • Martinez Cid, Waldyn Gerardo (University of Alabama Libraries, 2012)
    Ensemble methods, such as bagging (Breiman, 1996), boosting (Freund and Schapire, 1997) and random forests (Breiman, 2001) combine a large number of classifiers through (weighted) voting to produce strong classifiers. To ...

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