Abstract We present a discriminative learning algorithm for feed-forward Sum-Product Networks (SPNs) [42] and recurrent SPNs [31] based on the Extended Baum-Welch (EBW) algorithm [4] . We formulate the conditional data likelihood in the SPN framework as a rational function, and we use EBW to monotonically maximize it. We derive the algorithm for SPNs and RSPNs with both discrete and continuous variables. The experiments show that this algorithm performs better than both generative Expectation-Maximization, and discriminative gradient descent on a wide variety of applications. We also demonstrate the robustness of the algorithm in the case of missing features by comparing its performance to Support Vector Machines and Neural Networks.

中文翻译：

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通过扩展的 Baum-Welch 对前馈和循环和积网络进行判别训练

摘要 我们提出了一种基于扩展 Baum-Welch (EBW) 算法 [4] 的前馈和积网络 (SPN) [42] 和循环 SPN [31] 的判别学习算法。我们将 SPN 框架中的条件数据似然表述为有理函数，并使用 EBW 单调最大化它。我们推导出具有离散和连续变量的 SPN 和 RSPN 的算法。实验表明，该算法在各种应用中的性能都优于生成式期望最大化和判别式梯度下降。我们还通过将其性能与支持向量机和神经网络进行比较来证明算法在缺少特征的情况下的鲁棒性。