当类层次结构可用时使用基于层次结构的先验知识改进分类
Improving Classification When a Class Hierarchy is Available Using a Hierarchy-Based Prior
摘要：
我们引入了一种新的方法来建立分类模型时，我们有先验知识的类如何可以安排在一个层次结构中，基于它们是多么容易区分。新方法使用贝叶斯形式的多项logit（MNL，a.k.a. "softmax“）模型，具有在树中附近的类的参数之间引入相关性的先验。我们比较了新方法，普通的MNL模型，以及以不同方式使用的模型的模拟数据的性能。我们还测试了一个文档标签问题的新方法，并发现它比其他方法更好，特别是当训练数据量很小时。
英文摘要：
We introduce a new method for building classification models when we have prior knowledge of how the classes can be arranged in a hierarchy, based on how easily they can be distinguished. The new method uses a Bayesian form of the multinomial logit (MNL, a.k.a. ``softmax'') model, with a prior that introduces correlations between the parameters for classes that are nearby in the tree. We compare the performance on simulated data of the new method, the ordinary MNL model, and a model that uses the hierarchy in different way. We also test the new method on a document labelling problem, and find that it performs better than the other methods, particularly when the amount of training data is small.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
