Discriminative Methods for Learning with Dependent Outputs

具有相关输出的学习判别方法

• 批准号: 0412894
• 负责人: Thorsten Joachims
• 金额: $ 27万
• 依托单位: Cornell University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-08-15 至 2008-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0412894&HistoricalAwards=false
• 关键词: Discriminative; Methods; Learning; Dependent; Outputs

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.

在过去的十年中，对歧视性学习方法（例如支持向量机（SVM）和增强的研究）不仅在预测准确性方面，而且在理论理解和鲁棒性方面都提高了机器学习的状态。但是，到目前为止，几乎所有这项研究都仅限于分类和回归问题。但是，如果我们要预测的对象不是单个类或实际数字，也不是像树，序列或一组依赖性标签这样的复杂对象，该怎么办？这种问题无处不在，例如，自然语言解析，信息提取和文本分类。该项目将将非常成功的学习方法（特别是大利润方法）扩展到预测这种多元和相互依存的输出的问题。特别是，该项目将产生可以处理三种依赖性的方法：结构，相关性和归纳依赖性。该项目的智力优点是方法的发展，其基本理论以及可以处理和利用复杂产出中依赖性的有效算法。更广泛的影响将来自多个领域的应用工作（例如，生物信息学，计算语言学），以及通过公开可用于应用领域的教学和研究的算法的软件实现。