半监督多示例核

在多示例学习中引入利用未标记示例的机制，能降低训练的成本并提高学习器的泛化能力。当前半监督多示例学习算法大部分是基于对包中的每一个示例进行标记，把多示例学习转化为一个单示例半监督学习问题。考虑到包的类标记由包中示例及包的结构决定，提出一种直接在包层次上进行半监督学习的多示例学习算法。通过定义多示例核，利用所有包(有标记和未标记)计算包层次的图拉普拉斯矩阵，作为优化目标中的光滑性惩罚项。在多示例核所张成的RKHS空间中寻找最优解被归结为确定一个经过未标记数据修改的多示例核函数，它能直接用在经典的核学习方法上。在实验数据集上对算法进行了测试，并和已有的算法进行了比较。实验结果表明，基于半监督多示例核的算法能够使用更少量的训练数据而达到与监督学习算法同样的精度，在有标记数据集相同的情况下利用未标记数据能有效地提高学习器的泛化能力。

Semi-supervised Multi-instance Kernel

In multi-instance learning, mechanism of making use of unlabeled instance would cut down training cost and increase generalization ability of the learner. Current algorithms perform semi-supervised multi-instance learning mainly by labeling each instance in bags and transferring multi-instance learning problem to singlcinstance semi supervised ones. In this paper we introduced a bag-level semi supervised learning framework with the idea that bag's label is determined by its instances and structure. With definition of multi-instance kernel, all bags(labeled and unlabeled) were used to calculate bag-level graph laplacian, which is a penalization term added to the optimization goal. We turned this problem into an optimization problem in RKHS and got a modified multi instance kernel function by unlabeled data as result that can be directly used in traditional kernel learning framework. We performed experiment in ALOI and Internet image datasets and compareed it with related algorithms. Experiment result shows that the proposed method can get the same accuracy as supervised counterpart with less labeled bags, and with the same labeled training data set the proposed method is of higher generalization ability.