CISE Research Instrumentation: Active Learning for Text, Scene, and Biosequence Analysis

CISE 研究仪器：用于文本、场景和生物序列分析的主动学习

• 批准号: 9617307
• 负责人: Garrison Cottrell
• 金额: $ 4万
• 依托单位: University of California-San Diego
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1997
• 资助国家: 美国
• 起止时间: 1997-02-01 至 1999-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9617307&HistoricalAwards=false
• 关键词: CISE; Research; Instrumentation; Active; Learning

9617307 Cottrell, Garrison W. Charles Elkan, University of California, San Diego CISE Research Instrumentation: Active Learning for Text, Scene, and Biosequence Analysis Adaptation to changing environments and changing goals through autonomous learning is a central theme of research in the artificial intelligence laboratory at UCSD. The workstations, robots, and neural network hardware acquired through this grant will enable recently developed learning methods to be tested in four large-scale, real-world applications.-Learning Semantic Representations for Information Retrieval-Embedded Virtual Agents-MEME: A New Software Tool for Sequence Analysis-Real Time Face and Object Recognition in Embedded Agents. The first subproject will apply new algorithms for document rerepresentation and response to human feedback to gigabytes of text in the national TREC competition. Another project will port to the worldwide web software agents that learn and interact now in a simulated artificial life environment. A third project will scale up new software for finding patterns in DNA and protein sequences into a tool capable of autonomously analyzing entire genomes. Finally, a fourth project will validate new active vision methods for recognizing and tracking objects by implementing these methods in mobile robots with controllable cameras.

Cottrell, Garrison W. Charles Elkan，加州大学圣地亚哥分校CISE研究仪器：文本、场景和生物序列分析的主动学习通过自主学习适应不断变化的环境和目标是加州大学圣地亚哥分校人工智能实验室研究的中心主题。通过这笔拨款获得的工作站、机器人和神经网络硬件将使最近开发的学习方法能够在四个大规模的实际应用中进行测试。嵌入式虚拟代理- meme：一种用于序列分析的新软件工具-嵌入式代理中的实时人脸和对象识别第一个子项目将在全国TREC竞赛中应用新的算法，用于文档重现和响应人类对千兆字节文本的反馈。另一个项目将移植到全球网络软件代理，这些代理现在在模拟的人工生命环境中学习和交互。第三个项目将扩大用于寻找DNA和蛋白质序列模式的新软件的规模，使其成为能够自主分析整个基因组的工具。最后，第四个项目将通过在具有可控摄像头的移动机器人中实施这些方法来验证识别和跟踪物体的新主动视觉方法。