Youngdae Kim

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Learning ranking (or preference) functions has become an important data mining task in recent years, as various applications have been found in information retrieval. Among rank learning methods, ranking SVM has been favorably applied to various applications, e.g., optimizing search engines, improving data retrieval quality. In this paper, we first develop(More)
SVM (Support Vector Machine) is a well-established machine learning methodology popularly used for classification, regression, and ranking. Recently SVM has been actively researched for rank learning and applied to various applications including search engines or relevance feedback systems. A query in such systems is the ranking function <i>F</i> learned by(More)
BACKGROUND We evaluated the recent prevalence of serologic markers of hepatitis A virus (HAV) in South Korea. METHODS The study data were the results of 60 126 anti-HAV (total) tests and 30 786 anti-HAV IgM tests that were performed during April 2009 through March 2010 by the Eone Reference Laboratory at the request of 1935 institutions throughout Korea.(More)
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