Using Graphs of Classifiers to Impose Declarative Constraints on Semi-supervised Learning
Table 1: Datasets for link-based classification task.
['[EMPTY]', 'CiteSeer', 'Cora', 'PubMed']
For each dataset, we use 1,000 publications for testing, and at most 5,000 publications for training. Among the training publications, we randomly pick 20 as labeled examples for each class, and the remaining ones are used as unlabeled. After the examples are prepared, we employ ProPPR to learn multi-class classifiers with α=0.1. The maximum epoch number is 40, and we find the training usually converges in less than 10 epochs. Note that constraints are combined with equal weights, and we control the effect of different constraints by using different numbers of examples from them (details discussed later).