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中文摘要：
针对标记数据不足的多标签分类问题,提出一种新的半监督Boosting算法,即基于函数梯度下降方法给出一种半监督Boosting多标签分类的框架,并将非标记数据的条件熵作为一个正则化项引入分类模型。实验结果表明,对于多标签分类问题,新的半监督Boosting算法的分类效果随着非标记数据数量的增加而显著提高,在各方面都优于传统的监督Boosting算法
英文摘要：
For multi-label classification problem without enough labeled data,this paper proposed a new semi-supervised Boosting algorithm.It provided a semi-supervised general multi-label Boosting framework by using functional gradient descent method.It also used the conditional entropy as a regularization term on unlabeled data in classification model.Experimental result shows that the performance of the new semi-supervised Boosting algorithm can be improved by increasing unlabeled data;it also has a better result than traditional supervised Boosting algorithm by different measures
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