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Discriminative training of feed-forward and recurrent sum-product networks by extended Baum-Welch

机译：通过扩展Baum-Welch对前馈和经常性和产品网络的鉴别培训

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摘要

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. (C) 2020 Elsevier Inc. All rights reserved.
机译：我们提出了一种基于延伸的BAUM-WELCH（EBW）算法[4]的前馈总和 - 产品网络（SPN）[42] [42]和反复间SPNS [31]的判别学习算法[4]。我们将SPN框架中的条件数据似然为合理功能，我们使用EBW单调地最大化它。我们使用离散和连续变量导出SPN和RSPN的算法。实验表明，该算法比生成期望最大化更好，以及对各种应用的鉴别梯度下降。我们还通过比较其性能来支持向量机和神经网络来展示缺失特征的算法的稳健性。 （c）2020 Elsevier Inc.保留所有权利。

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