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Building Adaptive Computational Systems for Physiological and Biomedical Data

Abstract In recent years, machine learning and data mining technologies have received growing attention in several areas such as recommendation systems, natural language processing, speech and handwriting recognition, image processing and biomedical domain. Many of these applications which deal with physiological and biomedical data require person specific or person adaptive systems. The greatest challenge in developing such systems is the subject-dependent data variations or subject-based variability in physiological and biomedical data, which leads to difference in data distributions making the task of modeling these data, using traditional machine learning algorithms, complex and challenging. As a result, despite the wide application of machine l... (more)
Created Date 2013
Contributor Chattopadhyay, Rita (Author) / Panchanathan, Sethuraman (Advisor) / Ye, Jieping (Advisor) / Li, Baoxin (Committee member) / Santello, Marco (Committee member) / Arizona State University (Publisher)
Subject Computer science / Active Learning / Biomedical / Electromyography / Physiology / Subject based variability / Transfer Learning
Type Doctoral Dissertation
Extent 154 pages
Language English
Reuse Permissions All Rights Reserved
Note Ph.D. Computer Science 2013
Collaborating Institutions Graduate College / ASU Library
Additional Formats MODS / OAI Dublin Core / RIS

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Description Dissertation/Thesis