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

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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.