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

该研究将应用于一类模式识别应用，我们称之为主观模式识别，以人脸识别和基于内容的信息检索为典型例子。我们对大脑如何处理这类信息知之甚少；事实上，判断相似/不同的标准是主观的--通常需要人力来验证机器得出的任何结论。尽管每年都有新的算法被开发出来，但很多研究都遵循一个通用的“试错”程序：给定一个数据集，对于一个已知的算法，我们尝试各种参数；如果我们对精度不满意，我们尝试一个新的算法；然后对于新的算法，我们尝试各种参数；以此类推。该研究将产生一种新型的主观模式识别算法，该算法具有可预测的高稳定性，从而保证在高水平上执行(即准确率)。

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.