文件名称:
Multi-Label dimensionality Reduction
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上传时间: 2018-07-05
详细说明： Multi-Label dimensionality Reduction Multi-label learning concerns supervised learning problems in which each instance may be associated with multiple labels simultaneously. A key difference between multi-label learning and traditional binary or multi-class learning is that the labels in multi-label learning are not mutually exclusive. Multi-label learning arises in many real-world applications. For example, in web page categorization, a web page may contain multiple topics. In gene and protein function prediction, multiple fun ctional labels may be associated with each gene and protein, since an individual gene or protein usually performs multiple functions. In automated newswire categorization, multiple labels can be associated with a newswire story indicating its subject categories and the regional categories of reported events. Motivated by the increasing number of applications, multi-label learning has attracted significant attention in data mining and machine learning recently. ...展开收缩
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