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To capture the trends of concerned topics in specific field, people often use topic discovery methods to get this goal. The traditional topic discovery algorithms are generally divided into two types, text clustering algorithm and text topic model. The former lacks of attention on semantic information, and the latter always ignores relativity of the topic. These affect the topic discovery and topic...
Often in real-world applications such as web page categorization, automatic image annotations and protein function prediction, each instance is associated with multiple labels (categories) simultaneously. In addition, due to the labeling cost one usually deals with a large amount of unlabeled data while the fraction of labeled data points will typically be small. In this paper, we propose a multi-label...
Forecasting accurately is essential to successful inventory planning in retail. Unfortunately, there is not always enough historical data to forecast items individually- this is particularly true in e-commerce where there is a long tail of low selling items, and items are introduced and phased out quite frequently, unlike physical stores. In such scenarios, it is preferable to forecast items in well-designed...
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