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You searched for +publisher:"Rutgers University" +contributor:("CHAOVALITWONGSE, W. ART"). Showing records 1 – 2 of 2 total matches.

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Rutgers University

1. Gazzola, Gianluca. Supervised learning methods for variable importance and regression with uncertainty on dependent data.

Degree: PhD, Operations Research, 2019, Rutgers University

This dissertation covers a collection of supervised learning methods targeted to data with complex dependence patterns. Part of our work orbits around the concept of variable importance, that is, the relative contribution an input variable to the prediction or the explanation of an output variable. Our interest in variable importance, and its estimation, is two-fold. On the one hand, as a tool for the characterization of data sets produced by multi-stage systems, where variables are related to each other via a network of correlations and causal dependencies. On the other hand, as a tool for the selection of minimal input-variable subsets with optimal predictive performance, in a more general framework involving data sets with an interesting structure of inter-variable dependence and redundancy. The rest of our work focuses on the problem of function approximation in the presence of uncertainty, and, specifically, on the calculation of optimal interpolating hyperplanes from data represented by convex polyhedra, rather than points. In this context, we propose algorithms to determine the spatial orientation of such polyhedra based on the multivariate relationships observed in the data, with particular focus on missing-value scenarios. For all of our methods, we present successful validation on an extensive and diverse array of real-world and simulated problems.

Advisors/Committee Members: Jeong, Myong K (chair), Boros, Endre (internal member), BEN-ISRAEL, ADI (internal member), Packard, Norman (outside member), CHAOVALITWONGSE, W. ART (outside member), Tortorella, Michael (outside member), School of Graduate Studies.

Subjects/Keywords: Supervised learning (Machine learning)

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Gazzola, G. (2019). Supervised learning methods for variable importance and regression with uncertainty on dependent data. (Doctoral Dissertation). Rutgers University. Retrieved from https://rucore.libraries.rutgers.edu/rutgers-lib/60158/

Chicago Manual of Style (16th Edition):

Gazzola, Gianluca. “Supervised learning methods for variable importance and regression with uncertainty on dependent data.” 2019. Doctoral Dissertation, Rutgers University. Accessed January 19, 2020. https://rucore.libraries.rutgers.edu/rutgers-lib/60158/.

MLA Handbook (7th Edition):

Gazzola, Gianluca. “Supervised learning methods for variable importance and regression with uncertainty on dependent data.” 2019. Web. 19 Jan 2020.

Vancouver:

Gazzola G. Supervised learning methods for variable importance and regression with uncertainty on dependent data. [Internet] [Doctoral dissertation]. Rutgers University; 2019. [cited 2020 Jan 19]. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/60158/.

Council of Science Editors:

Gazzola G. Supervised learning methods for variable importance and regression with uncertainty on dependent data. [Doctoral Dissertation]. Rutgers University; 2019. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/60158/


Rutgers University

2. Iyigun, Cem. Probabilistic distance clustering.

Degree: PhD, Operations Research, 2008, Rutgers University

We present a new iterative method for probabilistic clustering of data. Given clusters, their centers, and the distances of data points from these centers, the probability of cluster membership at any point is assumed inversely proportional to the distance from (the center of) the cluster in question. This assumption is our working principle. The method is a generalization, to several centers, of the Weiszfeld method for solving the Fermat-Weber location problem. At each iteration, the distances (Euclidean, Mahalanobis, etc.) from the cluster centers are computed for all data points, and the centers are updated as convex combinations of these points, with weights determined by the above principle. Computations stop when the centers stop moving. Progress is monitored by the joint distance function (JDF), a measure of distance from all cluster centers, that evolves during the iterations, and captures the data in its low contours. There are problems where the cluster sizes are given (as in capacitated facility location problems) and there are problems where the cluster sizes are unknowns to be estimated. The probabilistic distance clustering approach works well in both cases. The probabilistic distance clustering method adjusted for cluster size (called PDQ method) method is described, and applied to location problems, and mixtures of distributions, where it is a viable alternative to the EM method. The method is simple, fast (requiring a small number of cheap iterations) and insensitive to outliers. An important issue in clustering is the "right"number of clusters that best fits a data set. The JDF is used successfully to settle this issue and determine the correct number of clusters for a given data set.

Advisors/Committee Members: Iyigun, Cem (author), Prekopa, Andras (chair), Boros, Endre (internal member), Ben-Israel, Adi (dissertation committee member), CHAOVALITWONGSE, W. ART (internal member), ARAV, MARINA (outside member).

Subjects/Keywords: Cluster analysis

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Iyigun, C. (2008). Probabilistic distance clustering. (Doctoral Dissertation). Rutgers University. Retrieved from http://hdl.rutgers.edu/1782.2/rucore10001600001.ETD.17142

Chicago Manual of Style (16th Edition):

Iyigun, Cem. “Probabilistic distance clustering.” 2008. Doctoral Dissertation, Rutgers University. Accessed January 19, 2020. http://hdl.rutgers.edu/1782.2/rucore10001600001.ETD.17142.

MLA Handbook (7th Edition):

Iyigun, Cem. “Probabilistic distance clustering.” 2008. Web. 19 Jan 2020.

Vancouver:

Iyigun C. Probabilistic distance clustering. [Internet] [Doctoral dissertation]. Rutgers University; 2008. [cited 2020 Jan 19]. Available from: http://hdl.rutgers.edu/1782.2/rucore10001600001.ETD.17142.

Council of Science Editors:

Iyigun C. Probabilistic distance clustering. [Doctoral Dissertation]. Rutgers University; 2008. Available from: http://hdl.rutgers.edu/1782.2/rucore10001600001.ETD.17142

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