Discriminative Methods for Learning with Dependent Outputs

在过去的十年里，对判别学习方法的研究，如支持向量机（svm）和boosting，不仅在预测精度方面，而且在理论理解和鲁棒性方面，都提高了机器学习的水平。然而，到目前为止，几乎所有的研究都局限于分类和回归问题。但是，如果我们想要预测的对象不是一个单一的类或实数，或者预测一个复杂的对象，如树、序列或一组依赖标签，该怎么办？例如，在自然语言解析、信息提取和文本分类中，这些问题是普遍存在的。该项目将扩展非常成功的学习方法——特别是像支持向量机（svm）这样的大利润方法——来预测这种多元和相互依赖的输出。特别地，这个项目将产生可以处理三种类型依赖的方法：结构、关联和归纳依赖。这个项目的智力价值在于方法的发展，它们的基础理论，以及有效的算法，可以处理和利用复杂输出中的依赖关系。更广泛的影响将来自几个领域的应用工作（例如，生物信息学，计算语言学），以及将算法的软件实现公开提供给应用领域的教学和研究。

Over the last decade, research on discriminative learning methods like support vector machines (SVMs) and boosting has raised the state of the art in machine learning not only with respect to prediction accuracy, but also in terms of theoretical understanding and robustness. However, so far almost all of this research has been limited to problems of classification and regression. But what if the object we want to predict is not a single class or a real number or predict a complex object like a tree, a sequence, or a set of dependent labels? Such problems are ubiquitous, for example, in natural language parsing, information extraction, and text classification. The project will extend highly successful learning methods--in particular large-margin methods like support vector machines (SVMs)--to the problem of predicting such multivariate and interdependent outputs. In particular, this project will produce methods that can handle three types of dependencies: structure, correlation, and inductive dependencies. The intellectual merit of this project is the development of methods, their underlying theory, and efficient algorithms that can handle and exploit dependencies in complex outputs. Broader impact will come from applied work in several domains (e.g., bioinformatics, computational linguistics), as well as from making software implementations of the algorithms publicly available for teaching and research in applied fields.