RI: Small: Boosting, Optimality and Game Theory

RI：小：Boosting、最优性和博弈论

• 批准号: 1016029
• 负责人: Robert Schapire
• 金额: $ 45万
• 依托单位: Princeton University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-01 至 2014-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1016029&HistoricalAwards=false
• 关键词: RI; Small; Boosting; Optimality; Game

Boosting is a machine-learning method based on combining manycarefully trained weak prediction rules into a single, highly accurateclassifier. Boosting has both a rich theory and a record of empiricalsuccess, for instance, to face detection and spoken-dialogue systems.The theory of boosting is broadly connected to other research fields,but has only been fully developed for the simplest learning problems.Nevertheless, in practice, boosting is commonly applied in settingswhere the theory lags well behind. We do not know if such practicalmethods are truly best possible; even for binary classification, it isnot clear how to best exploit what is known about how boostingoperates. New challenges will demand an even greater widening of thefoundations of boosting.The goal of this project is to develop broad theoretical insights andversatile algorithmic principles. The aim is to studygame-theoretically how to design the most efficient and effectiveboosting algorithms possible.Research on boosting is spread over many years. across multiplepublications and disciplines. To organize this body of work, asignificant activity of this project is the completion of a book onboosting which will provide a valuable resource for students andresearchers of diverse backgrounds and interests.Boosting has historically had a major impact on areas outside machinelearning, such as statistics, computer vision, and speech and languageprocessing. Thus, there is a strong potential for work at itsfoundations to have a broad impact on these other research andapplication areas as well.

Boosting是一种机器学习方法，它将许多精心训练的弱预测规则组合成一个高精度的分类器。 Boosting既有丰富的理论，也有成功的实践记录，例如，在人脸检测和口语对话系统中。Boosting的理论与其他研究领域有着广泛的联系，但只在最简单的学习问题上得到了充分的发展。然而，在实践中，Boosting通常应用于理论落后的环境中。 我们不知道这些实用的方法是否真的是最好的;即使是二元分类，也不清楚如何最好地利用关于提升如何运作的已知知识。 新的挑战将需要更大的提升基础。这个项目的目标是发展广泛的理论见解和通用的算法原理。 其目的是从博弈理论上研究如何设计最有效的提升算法。across跨multiplepublications出版物and disciplines学科. 为了组织这些工作，这个项目的一个重要活动是完成一本关于boosting的书，这本书将为不同背景和兴趣的学生和研究人员提供宝贵的资源。Boosting在历史上对机器学习之外的领域产生了重大影响，如统计学，计算机视觉，语音和语言处理。 因此，其基础工作也有很大的潜力对其他研究和应用领域产生广泛的影响。