论文标题
学习tversky的相似性
Learning Tversky Similarity
论文作者
论文摘要
在本文中，我们提倡Tversky的比率模型作为语义相似性的计算方法的适当基础，即以语义上有意义的方式比较诸如图像之类的对象。我们考虑了从适当的培训数据中学习Tversky相似性度量的问题，表明两个对象往往相似还是不同。在实验上，我们评估了两个图像数据集上的相似性学习方法，这表明与现有方法相比，其性能非常好。
In this paper, we advocate Tversky's ratio model as an appropriate basis for computational approaches to semantic similarity, that is, the comparison of objects such as images in a semantically meaningful way. We consider the problem of learning Tversky similarity measures from suitable training data indicating whether two objects tend to be similar or dissimilar. Experimentally, we evaluate our approach to similarity learning on two image datasets, showing that is performs very well compared to existing methods.
