RI: Learning Structure to Structure Mappings

提案0713483“RI：学习结构到结构映射“PI：Thorsten JoachimsCornell大学摘要本提案的目标是在支持向量机（SVM）框架中扩展正在进行的结构化输出空间学习工作。这样的结构化输出空间出现在预测不是单变量响应的问题中（例如，是/否），而是结构化对象（例如，序列、树或比对）。虽然最近的工作已经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所需的复杂结构（例如，机器翻译）。该项目旨在将结构SVM框架扩展到此类复杂结构。具体而言，它侧重于计算效率，更广泛的损失函数类，以及使用未标记的数据来提高统计效率所需的增益。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。这些将足够强大和高效，以便适用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两个高影响力的领域，如蛋白质结构预测或机器翻译。

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.