在线监督学习及其在社交网络节点分类中的应用
A Tutorial on Online Supervised Learning with Applications to Node Classification in Social Networks
摘要：
我们重温了T.封面'65，也许，并不像它应该的那样为更广泛的社区所熟知。本教程的第一个目标是解释-通过这个基本结果的棱镜-如何通过建模解决方案集而不是未知的数据生成机制来解决某些序列预测问题。我们扩大覆盖的观察在几个方向上，并专注于计算方面所提出的算法。几个例子，包括在网络中的节点分类的方法的适用性说明。本教程的第二个目的是证明以下现象：它是可能的预测以及组合的“基准”，我们有一定的乘法近似算法，即使精确计算的基准给定的所有数据是NP-难的。因此，所提出的预测方法，规避了一些计算困难与找到最佳模型给定的数据。当人们试图为基于图的问题或其他具有组合结构的问题开发概率模型时，这些困难会很快出现。
英文摘要：
We revisit the elegant observation of T. Cover '65 which, perhaps, is not as well-known to the broader community as it should be. The first goal of the tutorial is to explain---through the prism of this elementary result---how to solve certain sequence prediction problems by modeling sets of solutions rather than the unknown data-generating mechanism. We extend Cover's observation in several directions and focus on computational aspects of the proposed algorithms. The applicability of the methods is illustrated on several examples, including node classification in a network. The second aim of this tutorial is to demonstrate the following phenomenon: it is possible to predict as well as a combinatorial "benchmark" for which we have a certain multiplicative approximation algorithm, even if the exact computation of the benchmark given all the data is NP-hard. The proposed prediction methods, therefore, circumvent some of the computational difficulties associated with finding the best model given the data. These difficulties arise rather quickly when one attempts to develop a probabilistic model for graph-based or other problems with a combinatorial structure.
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。
