How much training data do you need?

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The Impact of Imbalanced Training Data for Convolutional Neural Networks Paulina Hensman and David Masko 摘要本论文从实验的角度调研了训练数据的不均衡性对采用CNN解决图像分类问题的性能影响.CIFAR-10数据集包含10个不同类别的60000个图像,用来构建不同类间分布的数据集.例如,一些训练集中包含一个类别的图像数目与其他类别的图像数目比例失衡.用这些训练集分别来训练一个CNN,度量其得…

How much training data do you need?

How much training data do you need? //@樵夫上校: 0. 经验上,10X规则(训练数据是模型参数量的10倍)适用与大多数模型,包括shallow network. 1.线性模型可以应用10X的经验规则,模型参数是特征选择后的数量(PCA等方法).2.NN可以将10X规则当做训练数据量的下限. The quality and amount of training data is often the single most dominant factor t…

什么情况下使用large training data会非常有效

收集大量的数据可能比算法的优劣更重要 Banko和Brill在2001年做了一个研究,是关于在句子中对易混单词进行识别,画出了上图的右边的那个图,这个图显示了对于不同的算法,它们的表现相似,但是随着training set size的增加,不同的算法的性能都增加.这个说明了一个较劣势的算法,如果它有大量的数据的话,在这个例子中,它的表现会对优秀的算法只有少量的数据要好.了解到这个情况,我们就知道了,在特定的情况下(数据量的提升对改进算法有效),我们应该把精力放在收集大量的数据上,而不是用来选择某…

论文解读（SR-GNN）《Shift-Robust GNNs: Overcoming the Limitations of Localized Graph Training Data》

论文信息论文标题:Shift-Robust GNNs: Overcoming the Limitations of Localized Graph Training Data论文作者:Qi Zhu, Natalia Ponomareva, Jiawei Han, Bryan Perozzi论文来源:2021, NeurIPS论文地址:download 论文代码:download 1 Introduction 半监督学习通过使用数据之间的关系(即边连接关系,会产生归纳偏差),以及一组带标签的样本…

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16 On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima 1609.04836v1

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Data Transformation / Learning with Counts

机器学习中离散特征的处理方法 Updated: August 25, 2016 Learning with counts is an efficient way to create a compact set of features for a dataset, based on counts of the values. You can use the modules in this section to build a set of counts and features, and late…