论文标题
基于统计模型的神经网络评估
Statistical model-based evaluation of neural networks
论文作者
论文摘要
使用基于统计模型的数据生成，我们开发了用于评估神经网络（NNS）的实验设置。该设置有助于基准基准一组NNS Vis-A-Vis最小均值欧元（MMSE）性能界限。这使我们能够测试训练数据大小，数据维度，数据几何形状，噪声以及训练和测试条件之间的不匹配的影响。在拟议的设置中，我们使用高斯混合物分布来生成训练和测试一组竞争NNS的数据。我们的实验表明了了解数据的适当应用和设计的数据类型和统计条件的重要性
Using a statistical model-based data generation, we develop an experimental setup for the evaluation of neural networks (NNs). The setup helps to benchmark a set of NNs vis-a-vis minimum-mean-square-error (MMSE) performance bounds. This allows us to test the effects of training data size, data dimension, data geometry, noise, and mismatch between training and testing conditions. In the proposed setup, we use a Gaussian mixture distribution to generate data for training and testing a set of competing NNs. Our experiments show the importance of understanding the type and statistical conditions of data for appropriate application and design of NNs
