TY - Jour A2 - De Maio，Carmen Au - Liu，Rong Au - Liu，延奥 - 燕，永港奥南，京燕PY - 2020DA - 2020 / 01/02迭代深社区：深度学习涉及输入数据点及其邻居的模型 - 9868017 VL - 2020 AB - 深度学习模型，如深度卷积神经网络和深度长期记忆模型，在阴影机学习中的许多模式分类应用中取得了巨大成功具有手工制作功能的模型。主要原因是深度学习模型通过多个神经元从大规模数据中提取分层特征的能力。然而，在许多其他情况下，由于模型输入的限制，现有的深度学习模型仍然无法获得令人满意的结果。现有的深度学习模型仅取得输入点的数据实例，但完全忽略数据集中的其他数据点，这可能提供对给定输入的分类的关键洞察力。To overcome this gap, in this paper, we show that the neighboring data points besides the input data point itself can boost the deep learning model’s performance significantly and design a novel deep learning model which takes both the data instances of an input point and its neighbors’ classification responses as inputs. In addition, we develop an iterative algorithm which updates the neighbors of data points according to the deep representations output by the deep learning model and the parameters of the deep learning model alternately. The proposed algorithm, named “Iterative Deep Neighborhood (IDN),” shows its advantages over the state-of-the-art deep learning models over tasks of image classification, text sentiment analysis, property price trend prediction, etc. SN - 1687-5265 UR - https://doi.org/10.1155/2020/9868017 DO - 10.1155/2020/9868017 JF - Computational Intelligence and Neuroscience PB - Hindawi KW - ER -