Theory of electoral college framework-based multi-classifier ensembling and its applications to subjective pattern recognition

基于选举团框架的多分类器集成理论及其在主观模式识别中的应用

• 批准号: 261403-2011
• 负责人: Chen, Liang
• 金额: $ 1.02万
• 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: 2013
• 资助国家: 加拿大
• 起止时间: 2013-01-01 至 2014-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=525204
• 关键词: Theory; electoral; college; framework; based

The proposed research will be applied to a class of pattern recognition applications, which we call subjective pattern recognition, with face identification and content-based information retrieval as typical examples. We know very little about how our brains process this type of information; indeed, the standards for judging the similarity/dissimilarity are subjective -- manpower is usually required to verify any conclusions made by machine. Although new algorithms are developed every year, much of the research follows a general "trial-and-error" procedure: Given a data set, for a known algorithm, we try various parameters; if we are not satisfied with the accuracy, we try a new algorithm; and then for the new algorithm, we try various parameters; and so on. The proposed research will lead to a new type of algorithm for subjective pattern recognition, which has predictable high stability and therefore is guaranteed to perform (i.e. accuracy) at a high level.

本文的研究将应用于一类模式识别的应用，我们称之为主观模式识别，人脸识别和基于内容的信息检索作为典型的例子。我们对大脑如何处理这类信息知之甚少;事实上，判断相似性/不相似性的标准是主观的-通常需要人工来验证机器得出的任何结论。虽然每年都有新的算法被开发出来，但大多数研究都遵循一个通用的“试错”过程：给定一个数据集，对于一个已知的算法，我们尝试各种参数;如果我们对准确性不满意，我们尝试一个新的算法;然后对于新算法，我们尝试各种参数;所提出的研究将导致用于主观模式识别的新型算法，其具有可预测的高稳定性，因此保证在高水平上执行（即，准确性）。