III-CXT-Medium: Interdisciplinary Machine Learning Research and Education

III-CXT-Medium：跨学科机器学习研究和教育

• 批准号: 0803409
• 负责人: Carla Brodley
• 金额: $ 87.58万
• 依托单位: Tufts University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-09-15 至 2013-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0803409&HistoricalAwards=false
• 关键词: III; CXT; Medium; Interdisciplinary; Machine

This project is encouraging interdisciplinary machine learning research and education that integrates the use of machine learning into the application areas, solves real science problems, and serves as a catalyst for new research in both machine learning and the application domains. We are investigating four basic machine learning issues that arise when working with real-world applications: (1) optimizing over choices available when generating training data; (2) assessing and improving the quality of training data; (3) designing specific algorithms and methods for time series and feature-based data; and (4) developing methods for abstaining during classification. Our research is motivated by on-going collaborations with researchers to create solutions for: training an artificial nose (Chemistry, Tufts); land-cover mapping from remotely sensed data (Geography, Boston University); classification of sky surveys (Astronomy, Harvard); non-invasive gluclose monitoring (Biomedical Engineering, Tufts); and liquification prediction (Civil Engineering, Tufts). The successful application of machine learning to each of the five tasks will have significant impact on the lives of humans. Our education initiatives have two complementary goals: (1) to educate computer science students on how to conduct interdisciplinary machine learning research; and (2) to educate professors, graduate students and undergraduates from science, engineering and medicine on how to recognize and pose problems as machine learning and data mining problems.

该项目鼓励跨学科的机器学习研究和教育，将机器学习的使用整合到应用领域，解决真实的科学问题，并作为机器学习和应用领域新研究的催化剂。 我们正在研究在使用真实世界应用程序时出现的四个基本机器学习问题：（1）在生成训练数据时优化可用的选择;（2）评估和提高训练数据的质量;（3）为时间序列和基于特征的数据设计特定的算法和方法;（4）开发分类过程中的弃权方法。 我们的研究是由与研究人员正在进行的合作，以创造解决方案：训练人工鼻子（化学，塔夫茨）;从遥感数据的土地覆盖映射（地理，波士顿大学）;天空调查的分类（天文学，哈佛）;非侵入性葡萄糖监测（生物医学工程，塔夫茨）;和液化预测（土木工程，塔夫茨）。 机器学习在这五项任务中的成功应用将对人类的生活产生重大影响。 我们的教育计划有两个互补的目标：（1）教育计算机科学专业的学生如何进行跨学科的机器学习研究;（2）教育来自科学，工程和医学的教授，研究生和本科生如何识别和提出机器学习和数据挖掘问题。