摘要翻译：
我们提出了一种将先验知识集成到鉴别分类器中的新框架。我们的框架允许像支持向量机这样的鉴别分类器利用生成设置中指定的先验知识。将拟合数据和尊重先验知识的双重目标化为双层规划，通过二阶锥规划迭代求解。为了验证我们的方法，我们考虑了使用WordNet（英语语言语义数据库）来提高新闻组分类的低样本分类精度的问题。WordNet被看作是一个近似的，但却是现成的背景知识来源，我们的框架能够以灵活的方式利用它。
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英文标题：
《Generative Prior Knowledge for Discriminative Classification》
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作者：
G. DeJong, A. Epshteyn
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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：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：
  We present a novel framework for integrating prior knowledge into discriminative classifiers. Our framework allows discriminative classifiers such as Support Vector Machines (SVMs) to utilize prior knowledge specified in the generative setting. The dual objective of fitting the data and respecting prior knowledge is formulated as a bilevel program, which is solved (approximately) via iterative application of second-order cone programming. To test our approach, we consider the problem of using WordNet (a semantic database of English language) to improve low-sample classification accuracy of newsgroup categorization. WordNet is viewed as an approximate, but readily available source of background knowledge, and our framework is capable of utilizing it in a flexible way.
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PDF链接：
https://arxiv.org/pdf/1109.6033