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.

翻译：本章考虑了在噪音面前学习线性阈值的计算和统计方面;在没有噪音的情况下,存在几种算法,利用少量数据有效地学习近乎最佳线性阈值;然而,即使少量对抗性噪音也使这个问题在最坏的情况下变得臭名昭著。我们讨论了通过利用数据生成过程的自然假设处理这些负面结果的方法。