使用随机多模型深度学习（RMDL）改进数据分类
An Improvement of Data Classification Using Random Multimodel Deep Learning (RMDL)
摘要：
每年复杂数据集数量的指数级增长需要机器学习方法的更多增强，以提供稳健和准确的数据分类。最近，与以前的机器学习算法相比，深度学习方法已经取得了超越的结果。然而，为这些模型找到合适的结构对研究人员来说是一个挑战。本文介绍了随机多模型深度学习（RMDL）：一种新的集成深度学习分类方法。RMDL解决了寻找最佳深度学习结构和架构的问题，同时通过集成深度学习架构来提高鲁棒性和准确性。简而言之，RMDL并行训练多个随机生成的深度神经网络（DNN），卷积神经网络（CNN）和递归神经网络（RNN）模型，并将其结果组合起来，以单独产生任何这些模型的更好结果。在本文中，我们描述了RMDL模型，并比较图像和文本分类以及人脸识别的结果。我们使用MNIST和CIFAR-10数据集作为图像分类的地面实况数据集，使用WOS、Reuters、IMDB和20新闻组数据集进行文本分类。最后，我们使用ORL数据集来比较模型在人脸识别任务上的性能。
英文摘要：
The exponential growth in the number of complex datasets every year requires more enhancement in machine learning methods to provide robust and accurate data classification. Lately, deep learning approaches have achieved surpassing results in comparison to previous machine learning algorithms. However, finding the suitable structure for these models has been a challenge for researchers. This paper introduces Random Multimodel Deep Learning (RMDL): a new ensemble, deep learning approach for classification. RMDL solves the problem of finding the best deep learning structure and architecture while simultaneously improving robustness and accuracy through ensembles of deep learning architectures. In short, RMDL trains multiple randomly generated models of Deep Neural Network (DNN), Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) in parallel and combines their results to produce better result of any of those models individually. In this paper, we describe RMDL model and compare the results for image and text classification as well as face recognition. We used MNIST and CIFAR-10 datasets as ground truth datasets for image classification and WOS, Reuters, IMDB, and 20newsgroup datasets for text classification. Lastly, we used ORL dataset to compare the model performance on face recognition task.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
