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This paper presents a new approach to a maximum a posteriori (MAP)-based classification, specifically, MAP-based kernel classification trained by linear programming (MAPLP). Unlike traditional MAP-based classifiers, MAPLP does not directly estimate a posterior probability for classification. Instead, it introduces a kernelized function to an objective(More)
1. In the experiment we used an open source package GNU Linear Programming Kit (GLPK) for optimization problem. 2. We conducted experiment by using 13 data sets from the UCI repository. Maximum a Posteriori (MAP) has been adopted and studied in pattern recognition for the purpose of classification. In MAP classifier the information of a posteriori(More)
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