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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)
Maximum a posteriori (MAP)-based kernel classification trained by linear programming (MAPLP) has previously been proposed as a new approach to MAP-based classification. As opposed to conventional MAP-based approaches, MAPLP does not directly estimate a posteriori probabilities for classification, but instead introduces its surrogate function to an objective(More)
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)
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