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In multi-label learning, feature selection is a non-ignorable preprocessing step which can alleviate the negative effect of high-dimensionality. To address this problem, a number of effective information theory based feature selection algorithms for multi-label learning are proposed. However, these existing algorithms assume that the label space of multi-label training data is complete. In practice,...
Collaborative filtering is one of widely used recommendation approaches to make recommendation services for users. The core of this approach is to improve capability for finding accurate and reliable neighbors of active users. However, collected data is extremely sparse in the user-item rating matrix, meanwhile many existing similarity measure methods using in collaborative filtering are not much...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.