Advisor

Anderson, Derek T.

Committee Member

Younan, Nicholas H.

Committee Member

Abdelwahed, Sherif.

Date of Degree

1-1-2015

Document Type

Graduate Thesis - Open Access

Degree Name

Master of Science

College

James Worth Bagley College of Engineering

Department

Department of Electrical and Computer Engineering

Abstract

This thesis aims to simultaneously minimize function error and model complexity for data fusion via the Choquet integral (CI). The CI is a generator function, i.e., it is parametric and yields a wealth of aggregation operators based on the specifics of the underlying fuzzy measure. It is often the case that we desire to learn a fusion from data and the goal is to have the smallest possible sum of squared error between the trained model and a set of labels. However, we also desire to learn as “simple’’ of solutions as possible. Herein, L1-norm regularization of a lexicographically encoded capacity vector relative to the CI is explored. The impact of regularization is explored in terms of what capacities and aggregation operators it induces under different common and extreme scenarios. Synthetic experiments are provided in order to illustrate the propositions and concepts put forth.

URI

https://hdl.handle.net/11668/16912

Comments

l1-norm regularization||capacity learning||minimum model complexity||fuzzy integral||Choquet integral

Share

COinS