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Conformal Predictions in Multimedia Pattern Recognition

Abstract The fields of pattern recognition and machine learning are on a fundamental quest to design systems that can learn the way humans do. One important aspect of human intelligence that has so far not been given sufficient attention is the capability of humans to express when they are certain about a decision, or when they are not. Machine learning techniques today are not yet fully equipped to be trusted with this critical task. This work seeks to address this fundamental knowledge gap. Existing approaches that provide a measure of confidence on a prediction such as learning algorithms based on the Bayesian theory or the Probably Approximately Correct theory require strong assumptions or often produce results that are not practical or reliable... (more)
Created Date 2010
Contributor Nallure Balasubramanian, Vineeth (Author) / Panchanathan, Sethuraman (Advisor) / Ye, Jieping (Committee member) / Li, Baoxin (Committee member) / Vovk, Vladimir (Committee member) / Arizona State University (Publisher)
Subject Computer Science / Artificial Intelligence / Information Science / Confidence estimation / Conformal predictions / Machine learning / Multimedia computing / Pattern recognition
Type Doctoral Dissertation
Extent 269 pages
Language English
Reuse Permissions All Rights Reserved
Note Ph.D. Computer Science 2010
Collaborating Institutions Graduate College / ASU Library
Additional Formats MODS / OAI Dublin Core / RIS

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Description Dissertation/Thesis