基于Laplacian谱下累积最大尺度面积的数据集复杂性评估
Dataset Complexity Assessment Based on Cumulative Maximum Scaled Area Under Laplacian Spectrum
摘要：
数据集复杂度评估的目的是在训练分类器之前通过复杂度计算来预测数据集的分类性能，也可以用于分类器选择和数据集约简。深度卷积神经网络（DCNN）的训练过程由于超参数不确定性和不同数据集引入的域移位而迭代且耗时。因此，在训练DCNN模型之前，通过有效地评估数据集的复杂性来预测分类性能是有意义的。本文提出了一种新的方法，称为累积最大缩放面积下拉普拉斯谱（cmsAULS），它可以实现最先进的复杂性评估性能的六个数据集。
英文摘要：
Dataset complexity assessment aims to predict classification performance on a dataset with complexity calculation before training a classifier, which can also be used for classifier selection and dataset reduction. The training process of deep convolutional neural networks (DCNNs) is iterative and time-consuming because of hyperparameter uncertainty and the domain shift introduced by different datasets. Hence, it is meaningful to predict classification performance by assessing the complexity of datasets effectively before training DCNN models. This paper proposes a novel method called cumulative maximum scaled Area Under Laplacian Spectrum (cmsAULS), which can achieve state-of-the-art complexity assessment performance on six datasets.
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。
