摘要翻译：
基于身份认证、入侵和垃圾邮件检测等应用，我们将单类分类(SCC)视为学习者和对手之间的两人博弈。在这个游戏中，学习者有一个来自目标分布的样本，目标是构造一个分类器，能够区分来自目标分布的观察和来自未知其他分布的观察。理想的SCC分类器必须保证对误报率（误报率）的容忍度，同时最小化误报率（入侵者通过率）。将SCC看作一个二人零和博弈，针对不同的博弈变体，我们分别确定了确定性和随机化的最优分类策略。我们证明了随机分类可以提供一个显著的优势。在确定性的情况下，我们给出了如何将SCC简化为两类分类，其中在两类问题中，另一类是一个综合生成的分布。给出了构造和求解这两类问题的一个高效实用的算法。该算法区分了目标分布的低密度区域，并证明了算法的一致性。
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英文标题：
《On the Foundations of Adversarial Single-Class Classification》
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作者：
Ran El-Yaniv and Mordechai Nisenson
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最新提交年份：
2010
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Machine Learning        机器学习
分类描述：Papers on all aspects of machine learning research (supervised, unsupervised, reinforcement learning, bandit problems, and so on) including also robustness, explanation, fairness, and methodology. cs.LG is also an appropriate primary category for applications of machine learning methods.
关于机器学习研究的所有方面的论文（有监督的，无监督的，强化学习，强盗问题，等等），包括健壮性，解释性，公平性和方法论。对于机器学习方法的应用，CS.LG也是一个合适的主要类别。
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一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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英文摘要：
  Motivated by authentication, intrusion and spam detection applications we consider single-class classification (SCC) as a two-person game between the learner and an adversary. In this game the learner has a sample from a target distribution and the goal is to construct a classifier capable of distinguishing observations from the target distribution from observations emitted from an unknown other distribution. The ideal SCC classifier must guarantee a given tolerance for the false-positive error (false alarm rate) while minimizing the false negative error (intruder pass rate). Viewing SCC as a two-person zero-sum game we identify both deterministic and randomized optimal classification strategies for different game variants. We demonstrate that randomized classification can provide a significant advantage. In the deterministic setting we show how to reduce SCC to two-class classification where in the two-class problem the other class is a synthetically generated distribution. We provide an efficient and practical algorithm for constructing and solving the two class problem. The algorithm distinguishes low density regions of the target distribution and is shown to be consistent.
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PDF链接：
https://arxiv.org/pdf/1010.4466