Study on Electoral College based Deep Learning and Its Applications

基于选举学院的深度学习及其应用研究

• 批准号: RGPIN-2016-06631
• 负责人: Chen, Liang
• 金额: $ 1.6万
• 依托单位: University of Northern British Columbia
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2016
• 资助国家: 加拿大
• 起止时间: 2016-01-01 至 2017-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=616423
• 关键词: Study; Electoral; College; based; Deep

On the theoretical front, we will study the stability of the Electoral College-based Deep Learning. Its shallowest form, Electoral College, has been used for many years in US presidential elections, and applied in areas such as image processing, pattern recognition, information retrieval, and other political elections. Our model will be the first in Artificial Intelligence research that illustrates the stability of a class of Deep Learning with respect to region/prefecture size, number of layers, and overlapping rates of neighboring regions in each layer. Our theory will guide applications on how to choose an adequate number of layers, an adequate region size as well as adequate overlapping rates of neighboring regions for each layer, for using Electoral College-based Deep Learning to improve the performance of pattern recognition and information retrieval approaches. Developments made on the theoretical front will be applied in two important fields: (1) A face image set-based surveillance system will be developed. This system will combine proven active infrared illumination technology for pupil location with a mathematical approach developed in this research program to match unknown faces to known faces on watch lists. The system is expected to be a significant improvement, when compared to any appearance-based face recognition system, in recognition rate vs. false acceptance rate, and in being disguise-proof. (2) An intelligent textual information retrieval system will be developed. This system will employ a novel approach, where a document is treated as a physical object which can be described from different "view angles", for document representation so that each document can be divided into overlapped blocks. A vector space model will be employed as the basic strategy for matching a query with "block" in a document, and the Electoral College-based Deep Learning will be employed in making retrieval decisions. The system is expected to have much better performance in precision vs. recall compared to other approaches. These systems can be further developed for use in national security systems and internet intelligent document retrieval systems respectively.

在理论方面，我们将研究基于选举学院的深度学习的稳定性。其最浅的形式，选举团，已被用于美国总统选举多年，并应用于图像处理，模式识别，信息检索和其他政治选举等领域。我们的模型将是人工智能研究中的第一个，它说明了一类深度学习在区域/地区大小、层数和每层相邻区域的重叠率方面的稳定性。我们的理论将指导应用程序如何选择足够的层数，足够的区域大小以及每个层的相邻区域的足够重叠率，以便使用基于选举学院的深度学习来提高模式识别和信息检索方法的性能。 理论前沿的发展将应用于两个重要领域：（1）将开发基于人脸图像集的监控系统。该系统将结合联合收割机证明主动红外照明技术的瞳孔定位与数学方法开发的这项研究计划，以匹配未知的面孔，以已知的面孔观察名单。与任何基于外观的人脸识别系统相比，该系统预计将在识别率与错误接受率以及防伪装方面有显着改进。(2)将开发一个智能文本信息检索系统。该系统将采用一种新颖的方法，将文件视为可以从不同“视角”描述的物理对象，用于文件表示，以便将每个文件划分为重叠的块。向量空间模型将被用作匹配查询与文档中的“块”的基本策略，并且基于选举学院的深度学习将被用于做出检索决策。与其他方法相比，该系统预计在精确度与召回率方面具有更好的性能。这些系统可进一步开发，分别用于国家安全系统和互联网智能文档检索系统。