文件名称:
learning-from-multi-label-data AT ECML PKDD 2009
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详细说明： This volume contains research papers accepted for presentation at the 1st International Workshop on Learning from Multi-Label Data (MLD’09), which will be held in Bled, Slovenia, at September 7, 2009 in conjunction with ECML/PKDD 2009 . MLD’09 is devoted to mult i-label learning, which is an emerging and promising research topic of machine learning. In multi-label learning, each example is associated with multiple labels simultaneously, which therefore encompasses traditional super- vised learning (single-label) as its special case. Multi-label learning is related to various machine learning paradigms, such as classification, ranking, semi-supervised learning, active learning, multi-instance learning, dimensionality reduction, etc. Initial attempts on multi-label learning date back to 1999 with works on multi-label text categorization. In recent years, the task of learning from multi-label data has been addressed by a number of methods adapted from various popular learning techniques, such as neural networks, decision trees, k-nearest neighbors, kernel methods, ensemble methods,etc.Moreimpressively,multi-labellearninghasmanifesteditseffectivenessin a diversity of real-world applications, such as image/video annotation, bioinformatics, websearchandmining,musiccategorization,collaborativetagging,directedmarketing, etc. ...展开收缩
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