论文标题
分类中的噪音
Noise in Classification
论文作者
论文摘要
本章考虑了在存在噪声的情况下学习线性阈值的计算和统计方面。当没有噪声时，存在几种算法，可以使用少量数据有效地学习近乎最佳的线性阈值。但是，即使是少量的对抗性噪声也使这个问题在最坏的情况下臭名昭著。我们通过利用对数据生成过程的自然假设来讨论处理这些负面结果的方法。
This chapter considers the computational and statistical aspects of learning linear thresholds in presence of noise. When there is no noise, several algorithms exist that efficiently learn near-optimal linear thresholds using a small amount of data. However, even a small amount of adversarial noise makes this problem notoriously hard in the worst-case. We discuss approaches for dealing with these negative results by exploiting natural assumptions on the data-generating process.
