论文标题
残留偶然神经网络具有深度正则化层用于二进制分类
Residual-Concatenate Neural Network with Deep Regularization Layers for Binary Classification
论文作者
论文摘要
许多复杂的深度学习模型都用于各种预后任务的不同变化。高等教育参数不一定确保很高的准确性。这可以通过考虑具有许多基于正则化技术的非常深的模型的变化来解决。在本文中，我们训练一个深层神经网络，该网络使用许多具有残留和串联过程的正则化层，以最佳地拟合多囊卵巢综合征诊断。该网络是从无法满足数据需求的每一步都进行的改进，并无缝地达到了99.3％的精度。
Many complex Deep Learning models are used with different variations for various prognostication tasks. The higher learning parameters not necessarily ensure great accuracy. This can be solved by considering changes in very deep models with many regularization based techniques. In this paper we train a deep neural network that uses many regularization layers with residual and concatenation process for best fit with Polycystic Ovary Syndrome Diagnosis prognostication. The network was built with improvements from every step of failure to meet the needs of the data and achieves an accuracy of 99.3% seamlessly.
