摘要翻译：
在本文中，我们提出了MIML（Multi-Instance Multi-Label learning）框架，其中一个示例由多个实例描述，并与多个类标签相关联。与传统的学习框架相比，MIML框架对于表示具有多种语义的复杂对象更加方便和自然。为了借鉴MIML的例子，我们提出了基于简单退化策略的MimlBoost和MimlSvm算法，实验表明在MIML框架中解决包含多语义复杂对象的问题可以获得良好的性能。考虑到退化过程可能会丢失信息，我们提出了D-MimlSvm算法，在正则化框架下直接处理MIML问题。此外，我们还表明，即使我们无法访问真实对象，因此无法使用MIML表示从真实对象中捕获更多信息，MIML仍然是有用的。我们提出了InsDif和SubCod算法。InsDif通过将单实例转换为用于学习的MIML表示来工作，而SubCod通过将单标签示例转换为用于学习的MIML表示来工作。实验表明，在某些任务中，它们能够比直接学习单实例或单标记示例获得更好的性能。
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英文标题：
《Multi-Instance Multi-Label Learning》
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作者：
Zhi-Hua Zhou, Min-Ling Zhang, Sheng-Jun Huang, Yu-Feng Li
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最新提交年份：
2011
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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：
  In this paper, we propose the MIML (Multi-Instance Multi-Label learning) framework where an example is described by multiple instances and associated with multiple class labels. Compared to traditional learning frameworks, the MIML framework is more convenient and natural for representing complicated objects which have multiple semantic meanings. To learn from MIML examples, we propose the MimlBoost and MimlSvm algorithms based on a simple degeneration strategy, and experiments show that solving problems involving complicated objects with multiple semantic meanings in the MIML framework can lead to good performance. Considering that the degeneration process may lose information, we propose the D-MimlSvm algorithm which tackles MIML problems directly in a regularization framework. Moreover, we show that even when we do not have access to the real objects and thus cannot capture more information from real objects by using the MIML representation, MIML is still useful. We propose the InsDif and SubCod algorithms. InsDif works by transforming single-instances into the MIML representation for learning, while SubCod works by transforming single-label examples into the MIML representation for learning. Experiments show that in some tasks they are able to achieve better performance than learning the single-instances or single-label examples directly.
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PDF链接：
https://arxiv.org/pdf/0808.3231