ITR: Collaborative Research: (ACS+NHS)-(dmc+soc): Machine Learning for Sequences and Structured Data: Tools for Non-Experts

ITR：协作研究：(ACS NHS)-(dmc soc)：序列和结构化数据的机器学习：非专家工具

• 批准号: 0427206
• 负责人: John Lafferty
• 金额: $ 33.31万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-09-01 至 2008-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0427206&HistoricalAwards=false
• 关键词: ITR; Collaborative; Research; ACS+NHS; dmc+soc

Sequential and graph-structured data arise naturally in a wide variety of scientific, engineering, and intelligence problems, such as handwriting and speech recognition, text mining, gene finding, and network analysis. While researchers have recently made significant progress on machine learning methods for processing structured data, these methods are much less accessible to scientists, engineers, and analysts than the better understood statistical learning techniques of classification and regression.This project is researching methods to advance the state of the art in machine learning for structured data, building on recent work in conditional random fields and weighted transducers. The project is also developing a software toolkit to make the results of these advances accessible to researchers working in a wide range of disciplines and application domains. The toolkit will enable users to define, train, and apply models for structured data without requiring advanced expertise in machine learning. The functionality of the toolkit will include methods for specifying features relevant to an application, automatically selecting the most relevant features, adjusting parameters to optimize suitable training objectives, and combining models that pertain to different facets of an application.The software, which will be freely distributed, will be tested with selected users in several application domains, and be carefully documented. The project will thus provide the scientific and engineering community with the first generally usable tool for learning from structured data, serving a role that is parallel to that of the more standard tools for classification and regression that are already widely used.

序列和图形结构的数据自然出现在各种科学、工程和智能问题中，如手写和语音识别、文本挖掘、基因发现和网络分析。虽然研究人员最近在处理结构化数据的机器学习方法方面取得了重大进展，但这些方法对科学家，工程师和分析师来说远不如更好理解的分类和回归统计学习技术。该项目正在研究方法，以推进结构化数据机器学习的最新技术，建立在条件随机场和加权换能器的最新工作基础上。 该项目还在开发一个软件工具包，使从事广泛学科和应用领域工作的研究人员能够利用这些进展的成果。 该工具包将使用户能够定义，训练和应用结构化数据模型，而无需机器学习方面的高级专业知识。 该工具包的功能将包括具体说明与某一应用有关的特征、自动选择最相关的特征、调整参数以优化适当的培训目标以及将与某一应用的不同方面有关的模型结合起来的方法，该软件将免费分发，将在若干应用领域中与选定的用户进行测试，并将仔细记录在案。 因此，该项目将为科学和工程界提供第一个普遍可用的工具，用于从结构化数据中学习，其作用与已经广泛使用的分类和回归的更标准工具的作用类似。