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A Study of Boosting based Transfer Learning for Activity and Gesture Recognition

Abstract Real-world environments are characterized by non-stationary and continuously evolving data. Learning a classification model on this data would require a framework that is able to adapt itself to newer circumstances. Under such circumstances, transfer learning has come to be a dependable methodology for improving classification performance with reduced training costs and without the need for explicit relearning from scratch. In this thesis, a novel instance transfer technique that adapts a "Cost-sensitive" variation of AdaBoost is presented. The method capitalizes on the theoretical and functional properties of AdaBoost to selectively reuse outdated training instances obtained from a "source" domain to effectively classif... (more)
Created Date 2011
Contributor Venkatesan, Ashok (Author) / Panchanathan, Sethuraman (Advisor) / Li, Baoxin (Committee member) / Ye, Jieping (Committee member) / Arizona State University (Publisher)
Subject Computer Science / Artificial Intelligence / Statistics / Activity Recognition / AdaBoost / Gesture Recognition / Machine Learning / Pattern Recogntion / Transfer Learning
Type Masters Thesis
Extent 97 pages
Language English
Reuse Permissions All Rights Reserved
Note M.S. Computer Science 2011
Collaborating Institutions Graduate College / ASU Library
Additional Formats MODS / OAI Dublin Core / RIS

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Description Dissertation/Thesis