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Robust Margin Based Classifiers For Small Sample Data

Abstract In many classication problems data samples cannot be collected easily, example in drug trials, biological experiments and study on cancer patients. In many situations the data set size is small and there are many outliers. When classifying such data, example cancer vs normal patients the consequences of mis-classication are probably more important than any other data type, because the data point could be a cancer patient or the classication decision could help determine what gene might be over expressed and perhaps a cause of cancer. These mis-classications are typically higher in the presence of outlier data points. The aim of this thesis is to develop a maximum margin classier that is suited to address the lack of robustness of discrimina... (more)
Created Date 2011
Contributor Gupta, Sidharth (Author) / Kim, Seungchan (Advisor) / Welfert, Bruno (Committee member) / Li, Baoxin (Committee member) / Arizona State University (Publisher)
Subject Computer Science / Statistics / Bioinformatics / Classifier / Overfitting / RSVM / Small Sample / SVM
Type Masters Thesis
Extent 46 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 Presentation on RSVM