计算机科学 ›› 2016, Vol. 43 ›› Issue (2): 287-292.doi: 10.11896/j.issn.1002-137X.2016.02.060
陈彤彤,丁昕苗,柳婵娟,邹海林,周树森,刘影
CHEN Tong-tong, DING Xin-miao, LIU Chan-juan, ZOU Hai-lin, ZHOU Shu-sen and LIU Ying
摘要: 多示例多标签学习是一种新型的机器学习框架。在多示例多标签学习中,样本以包的形式存在,一个包由多个示例组成,并被标记多个标签。以往的多示例多标签学习研究中,通常认为包中的示例是独立同分布的,但这个假设在实际应用中是很难保证的。为了利用包中示例的相关性特征,提出了一种基于示例非独立同分布的多示例多标签分类算法。该算法首先通过建立相关性矩阵表示出包内示例的相关关系,每个多示例包由一个相关性矩阵表示;然后建立基于不同尺度的相关性矩阵的核函数;最后考虑到不同标签的预测对应不同的核函数,引入多核学习构造并训练针对不同标签预测的多核SVM分类器。图像和文本数据集上的实验结果表明,该算法大大提高了多标签分类的准确性。
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