In this short note, we provide a sample complexity lower bound for learning linear predictors with respect to the squared loss. Our focus is on an agnostic setting, where no assumptions are made on the data distribution. This contrasts with standard results in the literature, which either make distributional assumptions, refer to specific parameter settings, or use other performance measures.

翻译：在本简短说明中,我们为学习关于平方损失的线性预测数据提供了较低的样本复杂性。我们的重点是不可知性设置,对数据分布没有作出假设。这与文献的标准结果形成对照,文献中的标准结果要么是分配假设,要么是提及具体的参数设置,要么是使用其他性能衡量尺度。