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We present a method of performing kernel space domain description of a dataset with incomplete entries without the need for imputation, allowing kernel features of a class of data with missing features to be rigorously described. This addresses the problem that absent data completion is usually required before kernel classifiers, such as support vector domain description (SVDD), can be applied; equally,...
Learning to rank is designed to determine a ranking for the target objects according to some rule. Specifically, the problem about learning to rank is to learn a ranking function from a training set whose data has been ranked. It is most applied to the social sciences and information retrieval. Learning to rank is a hot issue in the field of information retrieval and machine learning at present. This...
The multiclass classification problem has been applied to build a decision function to separate a set of data points into multiple classes. To solve this problem, a number of methods have been developed by extending binary classifications to multiclass classification. However, researches on how to effectively combine multiple hyperplanes to make a decision function are in its early stage. This paper...
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