ASU Electronic Theses and Dissertations

Permanent Link Feedback

Contributor
Date Range
2016 2016

In supervised learning, machine learning techniques can be applied to learn a model on a small set of labeled documents which can be used to classify a larger set of unknown documents. Machine learning techniques can be used to analyze a political scenario in a given society. A lot of research has been going on in this field to understand the interactions of various people in the society in response to actions taken by their organizations. This paper talks about understanding the Russian influence on people in Latvia. This is done by building an eeffective model learnt on initial set ...

Contributors
Bollapragada, Lakshmi Gayatri Niharika, Davulcu, Hasan, Sen, Arunabha, et al.
Created Date
2016

This collection includes most of the ASU Theses and Dissertations from 2011 to present. ASU Theses and Dissertations are available in downloadable PDF format; however, a small percentage of items are under embargo. Information about the dissertations/theses includes degree information, committee members, an abstract, supporting data or media.

In addition to the electronic theses found in the ASU Digital Repository, ASU Theses and Dissertations can be found in the ASU Library Catalog.

Dissertations and Theses granted by Arizona State University are archived and made available through a joint effort of the ASU Graduate College and the ASU Libraries.

For more information or questions about this collection contact or visit the Digital Repository ETD Library Guide or contact the ASU Graduate College at gradformat@asu.edu.