论文标题
非参数学习受限制的鲍尔茨曼机器
Non-Parametric Learning of Lifted Restricted Boltzmann Machines
论文作者
论文摘要
我们考虑在存在关系数据的情况下判别地学习受限制的Boltzmann机器的问题。与以前采用规则学习者（用于结构学习）的方法和依次的体重学习者（用于参数学习）不同，我们开发了一种同时执行的梯度提高方法。我们的方法学习了一组弱的关系回归树，其从根到叶子的路径是连接的子句，代表结构，其叶子值代表参数。当学习到的关系回归树被转换为升起的RBM时，其隐藏的节点正是从关系回归树中得出的结合条款。这导致了一个更容易解释的模型。我们的经验评估清楚地证明了这一方面，同时没有显示出学习模型的有效性损失。
We consider the problem of discriminatively learning restricted Boltzmann machines in the presence of relational data. Unlike previous approaches that employ a rule learner (for structure learning) and a weight learner (for parameter learning) sequentially, we develop a gradient-boosted approach that performs both simultaneously. Our approach learns a set of weak relational regression trees, whose paths from root to leaf are conjunctive clauses and represent the structure, and whose leaf values represent the parameters. When the learned relational regression trees are transformed into a lifted RBM, its hidden nodes are precisely the conjunctive clauses derived from the relational regression trees. This leads to a more interpretable and explainable model. Our empirical evaluations clearly demonstrate this aspect, while displaying no loss in effectiveness of the learned models.
