Convolutional Neural Networks for text categorization allows the extraction of features from the text represented through word embedding. The high dimensionality of the texts themselves implies a larger number of network parameters and a more complex optimization surface. Artificial neural network training is an NP-Hard optimization problem, which has been addressed by methods based on partial derivatives of the objective function and presents several theoretical and practical limitations, such as the probability of convergence to local minimums. In this work, we propose a hybrid method based on the Estimation of Distribution Algorithms for training a Convolutional Neural Network. For this, we train together gradient-based methods with the Estimation of Multivariate Normal Algorithm and Univariate Marginal Distribution Algorithm by dividing the training process into two stages. The different variants obtained with the proposed method are compared with gradient-based methods on public benchmark datasets and statistical differences are analyzed by nonparametric tests. The proposed method increases the accuracy of the convolutional network applied to the text categorization task and overcome in about 0.22%–24% the state-of-the-art algorithms.

用于文本分类的卷积神经网络允许从通过词嵌入表示的文本中提取特征。文本本身的高维度意味着更多的网络参数和更复杂的优化表面。人工神经网络训练是一个 NP-Hard 优化问题，已通过基于目标函数的偏导数的方法得到解决，并且存在一些理论和实践限制，例如收敛到局部最小值的概率。在这项工作中，我们提出了一种基于分布估计算法的混合方法，用于训练卷积神经网络。为此，我们通过将训练过程分为两个阶段，将基于梯度的方法与多元正态算法估计和单变量边际分布算法一起训练。在公共基准数据集上将使用该方法获得的不同变体与基于梯度的方法进行比较，并通过非参数测试分析统计差异。所提出的方法提高了卷积网络应用于文本分类任务，并以大约 0.22%–24% 的速度克服了最先进的算法。