A formula for multi-class distributed classifiers
Pattern Recognition Letters
The nature of statistical learning theory
The nature of statistical learning theory
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Classification by pairwise coupling
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Learning multi-category classification in bayesian framework
ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part I
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In this paper, a new method of composing a multi-classclassifier using pairwise classifiers is proposed. A"Resemblance Model" is exploited to calculate aposteriori probability for combining pairwise classifiers.We proved the validity of this model by usingapproximation of a posteriori probability formula. Usingthis theory, we can obtain the optimal decision. Anexperimental result of handwritten numeral recognition ispresented, supporting the effectiveness of our method.