CAREER: A Foundation for Applied Machine Learning

职业：应用机器学习的基础

• 批准号: 9733573
• 负责人: Carla Brodley
• 金额: $ 23.67万
• 依托单位: Purdue Research Foundation
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1998
• 资助国家: 美国
• 起止时间: 1998-09-01 至 2003-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9733573&HistoricalAwards=false
• 关键词: CAREER; Foundation; Applied; Machine; Learning

This project aims to provide a foundation for applying machine learning in practice. The research component has two goals. Firstly, to develop data collection and refinement methods that allow machine learning algorithm to extract structure from data. Secondly, to perform empirical and theoretical analyses of machine learning algorithms to develop a deeper understanding of current and new methods, and to uncover the characteristics of datasets that make them amenable to particular learning algorithms. The education component of this project aims to provide practitioners with the foundation required to apply machine learning successfully in practice by providing a deeper understanding of the type of structure that various learning algorithms can detect, by creating tools for collecting data, by giving students hands on experience applying machine learning in practice, and through inter-disciplinary research projects. These research and education activities are being performed in the context of several application domains, including mapping global land cover of the Earth from remotely sensed data, content-based image retrieval of high resolution CAT scans of the lung, learning to detect anomalies for computer security, and on-board processing of planetary data. In addition to its contribution to applied machine learning, this project has the benefit that the successful application of machine learning to proposed application domains will result in non-trivial contributions to medicine, computer systems development, and geography. http://mow.ecn.purdue.edu/ brodley/

该项目旨在为在实践中应用机器学习提供基础。 研究 组件有两个目标。首先，开发数据收集和细化方法，使机器 学习算法从数据中提取结构。 第二，进行实证分析和理论分析 机器学习算法，以加深对当前和新方法的理解， 揭示数据集的特征，使它们适合特定的学习算法。的 该项目的教育部分旨在为从业人员提供申请所需的基础。 通过提供对结构类型的更深入理解， 各种学习算法可以通过创建收集数据的工具， 在实践中应用机器学习的经验，并通过跨学科的研究项目。 这些 研究和教育活动正在几个应用领域的背景下进行， 包括利用遥感数据、基于内容的图像 检索肺部的高分辨率CAT扫描，学习检测计算机安全异常，以及 星上处理行星数据 除了对应用机器学习的贡献外， 该项目的好处是，机器学习在拟议应用领域的成功应用 将对医学、计算机系统开发和地理学做出重要贡献。 http://mow.ecn.purdue.edu/www.example.com