Titlebook: Machine Learning and Data Mining in Pattern Recognition; 8th International Co Petra Perner Conference proceedings 2012 Springer-Verlag Berl

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Bayesian Approach to the Concept Drift in the Pattern Recognition Problemshis paper proposes the mathematical and algorithmic framework for the concept drift in the pattern recognition problems. The probabilistic basis described in this paper is based on the Bayesian approach to the estimation of decision rule parameters. The pattern recognition procedure derived from thi

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Transductive Relational Classification in the Co-training Paradigming such data is prohibitive. Transductive learning, which learns from labeled as well as from unlabeled data already known at learning time, is highly suited to address this scenario. In this paper, we construct multi-views from a relational database, by considering different subsets of the tables

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Generalized Nonlinear Classification Model Based on Cross-Oriented Choquet Integraldel to achieve the classification boundaries which can classify data in such situation as one class surrounding another one in a high dimensional space. The values of unknown parameters in the generalized model are optimally determined by a genetic algorithm based on a given training data set. Both