CAREER: Optimal Information Extraction in Intelligent Systems

职业：智能系统中的最佳信息提取

• 批准号: 0133996
• 负责人: Virginia de Sa
• 金额: $ 45.54万
• 依托单位: University of California-San Diego
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2002
• 资助国家: 美国
• 起止时间: 2002-07-01 至 2009-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0133996&HistoricalAwards=false
• 关键词: CAREER; Optimal; Information; Extraction; Intelligent

This is a Faculty Early Career Development (CAREER) award. The research will explore how an organism extracts information from its environment for learning and perception, both to understand human learning and to create better machine learning algorithms. The first objective is to develop and apply new algorithms to better understand the mapping in the sensory pathways. An important goal is to understand how the visual pathway computes the invariant responses observed in inferotemporal cortex. The second objective is to study the extraction of information from cross-sensory interaction and its role in the development of perceptual invariance. This work will involve integrated computer simulations, mathematical modeling, and psychological experiments. As part of this goal, the researcher will study input feature selection, output feature selection, and the general problem of how dimensions should best interact in machine learning algorithms. The final research goal is to bring together the new knowledge in constructing a better autonomous learning machine that can learn to recognize objects. The algorithm will be more modular than current algorithms and will collect its own training data autonomously through a camera, microphone, and other sensors.The educational goal is to train students in the lab as well as in the classes to think about problems from a variety of approaches. They will be educated in the advantages and limitations of computational modeling, computational analysis, psychophysics and electrophysiology.This CAREER award recognizes and supports the early career-development activities of a teacher-scholar who is likely to become an academic leader of the twenty-first century. The research will improve our understanding of optimal integration between sensory modalities. This will lead to improvement in computer sensing algorithms, including computer vision, speech recognition, and any other application where other sources of information may be available. The work is also expected to give insight to the general problem of how to optimally combine different sources of information for machine learning. The educational aspects of this project are designed to give students a multidisciplinary perspective along with specific skills allowing them to use and appreciate a variety of approaches and techniques.

这是一个教师早期职业发展（CAREER）奖。 该研究将探索生物体如何从其环境中提取信息进行学习和感知，以了解人类学习和创建更好的机器学习算法。 第一个目标是开发和应用新的算法，以更好地理解感觉通路中的映射。 一个重要的目标是了解视觉通路如何计算颞下皮层中观察到的不变反应。 第二个目标是研究从跨感觉交互中提取信息及其在知觉不变性发展中的作用。 这项工作将涉及综合计算机模拟，数学建模和心理实验。 作为这一目标的一部分，研究人员将研究输入特征选择，输出特征选择，以及维度如何在机器学习算法中最好地相互作用的一般问题。 最终的研究目标是将新的知识整合在一起，构建一个更好的自主学习机器，可以学习识别对象。 该算法将比当前的算法更加模块化，并将通过摄像头、麦克风和其他传感器自动收集自己的训练数据。教育目标是在实验室和课堂上培养学生从各种方法思考问题。 他们将在计算建模，计算分析，心理物理学和电生理学的优点和局限性的教育。这个职业生涯奖承认和支持的教师学者谁是可能成为一个学术领袖的21世纪的早期职业发展活动。 该研究将提高我们对感觉模态之间的最佳整合的理解。 这将导致计算机传感算法的改进，包括计算机视觉、语音识别和其他信息源可用的任何其他应用。 这项工作还有望深入了解如何最佳地将联合收割机不同的信息源结合起来进行机器学习的一般问题。 该项目的教育方面旨在为学生提供多学科的视角，沿着特定技能，使他们能够使用和欣赏各种方法和技术。