RI: Learning Structure to Structure Mappings

提案0713483“RI：从学习结构到结构映射”PI：Thorsten Joachims Cornell University摘要这项提案的目标是在支持向量机框架中扩展正在进行的利用结构化输出空间进行学习的工作。这种结构化输出空间出现在预测不是单变量响应(例如，是/否)而是结构化对象(例如，序列、树或比对)的问题中。虽然最近的工作揭示了如何有区别地学习具有有限相互依赖性的简单结构的预测规则，但需要研究将这些方法扩展到许多应用(例如，机器翻译)所需的复杂结构。该项目旨在将结构支持向量机框架扩展到这样的复杂结构。具体地说，它侧重于计算效率所需的收益、更广泛的损失函数类别以及使用未标记数据来提高统计效率。与过去一样，该项目计划提供项目中开发的方法的软件实现。这些将得到足够的健壮和有效率，以便适合于机器学习研究社区以外的现实世界应用以及课堂教学。该项目将把其结果应用于蛋白质结构预测或机器翻译等两个影响较大的领域。

Proposal 0713483"RI: Learning Structure to Structure Mappings"PI: Thorsten JoachimsCornell UniversityABSTRACTThis goal of this proposal is to extend ongoing work on learning with structured output spaces in the support-vector-machine (SVM) framework. Such structured output spaces arise in problems where the prediction is not a univariate response (e.g., yes/no), but a structured object (e.g., a sequence, tree, or alignment). While recent work has uncovered how to discriminatively learn prediction rules for simple structures with limited interdependencies, research is needed to extend these methods to the complex structures needed for many applications (e.g., machine translation). This project aims to extend the structural SVM framework to such complex structures. Specifically, it focuses on the required gains in computational efficiency, broader classes of loss functions, and the use of unlabeled data to improve statistical efficiency. As done in the past, the project plans to make available software implementations of the methods developed in the project. These will be made sufficiently robust and efficient so as to be suitable for real-world applications outside the machine learning research community as well as for classroom teaching. The project will apply its results to two high-impact areas like protein structure prediction or machine translation.