We introduce online probabilistic label trees (OPLTs), an algorithm that trains a label tree classifier in a fully online manner without any prior knowledge about the number of training instances, their features and labels. OPLTs are characterized by low time and space complexity as well as strong theoretical guarantees. They can be used for online multi-label and multi-class classification, including the very challenging scenarios of one- or few-shot learning. We demonstrate the attractiveness of OPLTs in a wide empirical study on several instances of the tasks mentioned above.

翻译：我们引入了在线概率标签树(OPLTs)这一算法,这种算法以完全在线的方式培训标签树分类员,而事先对培训案例的数量、其特点和标签没有任何了解,OPLTs的特点是时间和空间复杂程度低,而且有很强的理论保障,可以用于在线多标签和多级分类,包括一或几集学习的极具挑战性的情景,我们在对上述几项任务的广泛实证研究中显示了OPLTs的吸引力。