We consider the problem of learning Relational Logistic Regression (RLR). Unlike standard logistic regression, the features of RLR are first-order formulae with associated weight vectors instead of scalar weights. We turn the problem of learning RLR to learning these vector-weighted formulae and develop a learning algorithm based on the recently successful functional-gradient boosting methods for probabilistic logic models. We derive the functional gradients and show how weights can be learned simultaneously in an efficient manner. Our empirical evaluation on standard data sets demonstrates the superiority of our approach over other methods for learning RLR.

我们考虑学习关系逻辑回归（RLR）的问题。与标准逻辑回归不同，RLR 的特征是具有相关权重向量的一阶公式，而不是标量权重。我们将学习 RLR 的问题转化为学习这些向量加权公式，并基于最近成功的概率逻辑模型的函数梯度提升方法开发了一种学习算法。我们推导了函数梯度并展示了如何以有效的方式同时学习权重。我们对标准数据集的实证评估表明我们的方法优于其他学习 RLR 的方法。