Online Max-Margin Weight Learning for Markov Logic Networks
Online Max-Margin Weight Learning for Markov Logic Networks
复制标题
马尔可夫逻辑网络的在线最大边际权重学习
DOI：
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发表时间：
2011
期刊：
影响因子：
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通讯作者：
R. Mooney
中科院分区：
文献类型：
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作者：
Tuyen N. Huynh;R. Mooney
大多数现有的马尔可夫逻辑网络（MLN）的权重学习算法使用批量训练，这对于非常大的数据集来说变得计算昂贵甚至不可行，因为训练示例可能不适合主存。为了克服这个问题，以前的工作已经使用在线学习算法来学习MLN的权重。然而，这项先前的工作只适用于现有的在线算法，并没有全面的研究MLN的在线权重学习。在本文中，我们推导出一个新的在线算法的结构化预测使用的原始-对偶框架，将其应用到学习MLN的权重，并与现有的在线算法在三个大型的，真实世界的数据集进行比较。实验结果表明，我们的新算法一般实现更好的准确性比现有的方法，特别是在噪声数据集。
Most of the existing weight-learning algorithms for Markov Logic Networks (MLNs) use batch training which becomes computationally expensive and even infeasible for very large datasets since the training examples may not fit in main memory. To overcome this problem, previous work has used online learning algorithms to learn weights for MLNs. However, this prior work has only applied existing online algorithms, and there is no comprehensive study of online weight learning for MLNs. In this paper, we derive a new online algorithm for structured prediction using the primal-dual framework, apply it to learn weights for MLNs, and compare against existing online algorithms on three large, real-world datasets. The experimental results show that our new algorithm generally achieves better accuracy than existing methods, especially on noisy datasets.