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Ordinal Data Classification Using Kernel Discriminant Analysis: A Comparison of Three Approaches
2012
2012 11th International Conference on Machine Learning and Applications
Ordinal data classification (ODC) has a wide range of applications in areas where human evaluation plays an important role, ranging from psychology and medicine to information retrieval. In ODC the output variable has a natural order; however, there is not a precise notion of the distance between classes. The recently proposed method for ordinal data, Kernel Discriminant Learning Ordinal Regression (KDLOR), is based on Linear Discriminant Analysis (LDA), a simple tool for classification. KDLOR
doi:10.1109/icmla.2012.86
dblp:conf/icmla/CardosoSD12
fatcat:mou2yojnffak3makzmsthrgvde