属性噪声鲁棒的二分类

Nov, 2019

Attribute noise robust binary classification

Aditya Petety, Sandhya Tripathi, N Hemachandra

TL;DR研究学习线性分类器时，当特征和标签都是二元时，考虑噪声干扰问题。研究了两种不同的属性噪声模型，并发现当噪声率较低到中等时，平方误差损失是鲁棒的。

Abstract

We consider the problem of learning linear classifiers when both features and labels are binary. In addition, the features are noisy, i.e., they could be flipped with an unknown probability. In Sy-De attribute no