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.

这是教师早期职业发展（职业）奖。 该研究将探讨生物体如何从其学习和感知的环境中提取信息，以了解人类学习并创建更好的机器学习算法。 第一个目标是开发和应用新算法，以更好地了解感觉途径中的映射。 一个重要的目标是了解视觉途径如何计算中颞皮层中观察到的不变反应。 第二个目标是研究信息从跨感官相互作用中提取及其在感知不变性发展中的作用。 这项工作将涉及集成的计算机模拟，数学建模和心理实验。 作为此目标的一部分，研究人员将研究输入功能选择，输出特征选择以及尺寸应如何在机器学习算法中最好相互作用的一般问题。 最终的研究目标是将新知识汇集在一起​​，以构建可以学会识别对象的更好的自主学习机器。 该算法将比当前算法更模块化，并将通过相机，麦克风和其他传感器自动收集自己的培训数据。教育目标是培训实验室和课堂上的学生，以考虑各种方法的问题。 他们将在计算建模，计算分析，心理物理学和电生理学的优势和局限性方面受过教育。该职业奖表彰并支持教师 - charlor的早期职业发展活动，他们可能成为二十一世纪的学术领导者。 这项研究将提高我们对感觉方式之间最佳整合的理解。 这将导致计算机传感算法的改进，包括计算机视觉，语音识别以及其他可能可用的信息来源的其他应用程序。 预计这项工作还可以深入了解如何最佳地结合机器学习的不同信息来源的一般问题。 该项目的教育方面旨在为学生提供多学科的观点以及特定的技能，使他们可以使用并欣赏各种方法和技术。