Face Recognition by Elastic Bunch Graph Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
The FERET Evaluation Methodology for Face-Recognition Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Convex Optimization
Mixed group ranks: preference and confidence in classifier combination
IEEE Transactions on Pattern Analysis and Machine Intelligence
Probabilistic model supported rank aggregation for the semantic concept detection in video
Proceedings of the 6th ACM international conference on Image and video retrieval
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Mixed Group Ranks is a parametric method for combining rank based classifiers that is effective for many-class problems. Its parametric structure combines qualities of voting methods with best rank approaches. In [1] the parameters of MGR were estimated using a logistic loss function. In this paper we describe how MGR can be cast as a probability model. In particular we show that using an exponential probability model, an algorithm for efficient maximum likelihood estimation of its parameters can be devised. While casting MGR as an exponential probability model offers provable asymptotic properties (consistency), the interpretability of probabilities allows for flexiblity and natural integration of MGR mixture models.